# A Closer Look at the Limitations of Instruction Tuning

Sreyan Ghosh  Chandra Kiran Reddy Evuru  Sonal Kumar  Ramaneswaran S  Deepali
Aneja  Zeyu Jin  Ramani Duraiswami  Dinesh Manocha

###### Abstract

Instruction Tuning (IT), the process of training large language models (LLMs)
using instruction-response pairs, has emerged as the predominant method for
transforming base pre-trained LLMs into open-domain conversational agents.
While IT has achieved notable success and widespread adoption, its limitations
and shortcomings remain underexplored. In this paper, through rigorous
experiments and an in-depth analysis of the changes LLMs undergo through IT,
we reveal various limitations of IT. In particular, we show that (1) IT fails
to enhance knowledge or skills in LLMs. LoRA fine-tuning is limited to
learning response initiation and style tokens, and full-parameter fine-tuning
leads to knowledge degradation. (2) Copying response patterns from IT datasets
derived from knowledgeable sources leads to a decline in response quality. (3)
Full-parameter fine-tuning increases hallucination by inaccurately borrowing
tokens from conceptually similar instances in the IT dataset for generating
responses. (4) Popular methods to improve IT do not lead to performance
improvements over a simple LoRA fine-tuned model. Our findings reveal that
responses generated solely from pre-trained knowledge consistently outperform
responses by models that learn any form of new knowledge from IT on open-
source datasets. We hope the insights and challenges revealed inspire future
work.

Machine Learning, ICML

##  1 Introduction

Large Language Models (LLMs) pre-trained at an incredible scale with the next
token prediction objective implicitly compress world knowledge in their
parameters (Zhao et al., 2023). These models learn general-purpose
representations, which can then be aligned with the desired response
characteristics (Zhang et al., 2023a). In the recent past, various methods for
aligning LLMs have been proposed, out of which instruction tuning (IT) (Wei et
al., 2022) and reinforcement learning from human feedback (RLHF) (Bai et al.,
2022) have gained the most popularity. IT, the process of fine-tuning an LLM
with instruction-response pairs, enables it to follow or complete tasks
instructed by humans. On the other hand, RLHF continually tunes an IT-ed LLM
to further align with human preference. While RLHF is prohibitively expensive
due to the requirement of large amounts of human-preference data (Bai et al.,
2022), IT with standard supervised loss has proved to be a more prevalent
technique for alignment (Zhou et al., 2023). IT-based alignment has led to
significant improvements in LLMs, unlocking impressive capabilities (Bubeck et
al., 2023), suggesting that fine-tuning is key to building and improving LLM-
based conversational agents. For the remainder of the paper, we
interchangeably refer to an IT-ed model as a fine-tuned model and the process
of IT interchangeably as fine-tuning.

Early work on IT focused on fine-tuning and evaluating LLMs with popular
natural language processing (NLP) tasks where the dataset instances are
phrased as natural language instructions. However, solely gauging the impact
of IT on an LLM using conventional NLP tasks and metrics falls short in
comprehensively assessing the array of its abilities, such as reasoning and
knowledge sharing, usually demanded by the diverse range of tasks encountered
by open-domain conversational agents (Wang et al., 2023). Since the colossal
success of ChatGPT (OpenAI, 2023), a more recent line of work aims at
evaluating and improving IT with open-domain instruction following data (Zheng
et al., 2023). However, though IT has been shown to achieve remarkable
improvement in performance and generalization to unseen NLP tasks, its
limitations and shortcomings have rarely been explored. We attribute this to
three main reasons: (1) Lack of comprehensive evaluation metrics for
evaluating open-domain instruction following abilities. (2) Lack of clear
understanding of the exact transformation a base pre-trained LLM goes through
with IT, and (3) The rapid rise of powerful semi-open-source chat models
(fine-tuned LLMs that don’t reveal their IT data) encourages the use of these
models merely as tools, leading to an under-exploration of several critical
elements in their development.

Main Contributions. In this paper, we investigate and reveal several
limitations of IT. To achieve this, we closely study the transformation a base
pre-trained model goes through after IT by experimenting with various open-
source IT datasets, LLMs, and training paradigms. Additionally, for
evaluation, we conduct a combination of expert human evaluation, GPT-4-based
multi-aspect evaluation, and token distribution analysis (Lin et al., 2023).
Our study explicitly focuses on evaluating the effectiveness of IT in
developing open-domain conversational agents (also commonly known as chat
models) and is limited to single-turn interactions. Our extensive results
reveal the following:

  1. 1.

IT is not a knowledge 111We use the term “knowledge” to encompass both the
factual information and task execution skills possessed by a model. enhancer.
Similar to concurrent work (Gudibande et al., 2023), we first find that IT
does not act as a knowledge enhancer at its current open-source scale. To dig
deeper, we compare the token distributions (explained further in Section 3)
between base LLMs and their IT-ed versions and find that LoRA (Hu et al.,
2021), which only teaches response initiation and extracts most of the
response from the pre-trained knowledge itself, leads to the most factually
correct responses. On the other hand, full-parameter fine-tuning leads to
knowledge degradation and reduces overall response quality.

  2. 2.

Pattern-copying often hurts performance. We first show that models IT-ed using
LoRA and full-parameter fine-tuning learn pattern-copying very differently.
While the former learns just stylistic tokens, the latter leads the model to
adapt more deeply to the specifics of the new training data. Next, we show
that while pattern-copying sometimes has some advantages, like detailed and
comprehensive answering, most of the time, it hurts the factual correctness of
the response. Finally, we propose a simple solution to overcome this.

  3. 3.

Full fine-tuning leads to knowledge degradation by increasing chances of
hallucination. These hallucinations are tokens borrowed from the IT dataset.
We show that when a model hallucinates or outputs incorrect tokens in its
responses, it is highly probable that these tokens are borrowed from instances
with similar concepts in the IT dataset itself. We further study this from the
lens of causal analysis. This effect is more prevalent in models trained using
full-parameter fine-tuning than in LoRA fine-tuning.

  4. 4.

Various methods to improve IT, proposed in literature, do not improve model
performance. We compare several methods like NEFTune (Jain et al., 2023) and
dataset filtering (Chen et al., 2023) on common grounds and show that, while
these methods improve over a full-finetuned model, a LoRA fine-tuned model
outperforms all of them. Consequently, these methods do not contribute to
knowledge advancement, and models leveraging pre-trained knowledge remain
superior.

Figure 1: Token distribution shift after IT. We compare token distributions
between base pre-trained models and their IT-ed versions using 3 metrics
defined in Section 3. We show that (1) Overall, LFT experiences low token
distribution shifts, indicating high alignment with pre-trained knowledge. (2)
Shifts in SFT are much higher than in LFT. (3) LFT is unaffected by the scale
of the IT dataset.

##  2 Experimental Setting

LLMs. For the scope of our analysis, we experiment with 5 different types of
LLMs, namely LLaMa-2 7B (Touvron et al., 2023), LLaMa-2 13B, LLaMa-2 70B,
Mistral-v0.1 7B (Jiang et al., 2023) and Phi-1.5 1.3B (Li et al., 2023b). We
use only the base pre-trained versions of the models (and not the chat
variants) and fine-tune them with IT ourselves. We employ LLaMa-2 70B only in
a fraction of experiments owing to compute constraints.

Fine-tuning Datasets. For fine-tuning with IT, we experiment with various
synthetic and human-written IT datasets. For synthetic, we use Alpaca 52k with
open-domain instruction-response pairs, constructed by prompting ChatGPT with
an initial seed dataset with few samples (Taori et al., 2023) and MedInstruct
52k from the medical domain constructed in similar fashion (Zhang et al.,
2023b). For human-written , we use LIMA 1K (Zhou et al., 2023) and databricks-
dolly 15k (Conover et al., 2023). Finally, we also use Tulu-V2-Mix 326k
(Ivison et al., 2023), which is an amalgamation of various open-source
datasets.

Evaluation Datasets. For evaluation, we primarily employ just-eval-instruct 1k
(Lin et al., 2023), which is an amalgamation of various open-source IT
evaluation sets and is tagged with various task types and topics (statistics
in Appendix C). This choice is motivated by its diverse and succinct nature,
which facilitates our efforts in conducting thorough, expert human
evaluations. We employ only the first 800 instances and remove the last 200,
as safety alignment is beyond the scope of this paper. For models fine-tuned
on MedInstruct 52k, we evaluate the model on MedInstruct-test 216 unless
stated otherwise. We do not evaluate the Open LLM Leaderboard (Beeching et
al., 2023) as it does not fit our primary motivation of evaluating LLMs on
their free-form open-domain instruction following capabilities.

Fine-tuning paradigms. For fine-tuning with IT, we employ either LoRA fine-
tuning (LFT) or (standard) full-parameter fine-tuning (SFT). LFT works by
approximating the model’s weight matrices with low-rank matrices. This reduces
the number of parameters that need to be fine-tuned, which makes the fine-
tuning process faster and more efficient. On the other hand, SFT, similar to
generic ERM, works by adjusting all or most of the model’s weights.

Evaluation. For evaluating our fine-tuned models, we perform a mix of expert
human evaluation and automatic evaluation using GPT-4 Turbo. Recent research
indicates that using ChatGPT and GPT-4 to score and assess outputs from LLMs
aligns well with human evaluations, offering the added benefit of cost
reduction (Liu et al., 2023; Li et al., 2023a; Chan et al., 2023; Xu et al.,
2023b; Zhou et al., 2023). We borrow the explainable and multi-aspect
evaluation framework from Lin et al. (2023), which prompts GPT-4 to assign a
score between 1 and 5 to an LLM’s response to an instruction on five aspects:
helpfulness, clarity, factuality, depth, and engagement. It also provides an
explanation for each score. Prompt can be found in Appendix A.

Naming Convention. We primarily follow the naming convention of
“model_dataset_training-paradigm”. For e.g., a LLaMa-2 7B trained on Alpaca
52k with SFT would be named as LLaMa-2 7B$\\_$SFT$\\_$Alpaca 52k.

Training and Evaluation hyper-parameters. All models are trained in a
distributed manner for 3 epochs, with a learning rate of 5e-5 and an effective
batch size of 32 (Taori et al., 2023). For LFT, we use a rank of 8 as Zhang et
al. (2024) shows that scaling rank is ineffective for LFT. For generation, we
employ greedy decoding (i.e., zero temperature) in all experiments.

Note: The main paper only presents results on the LLaMa-2 family, and results
on fine-tuning additional LLMs and already fine-tuned LLMs available open-
source, are provided in Section D.3. All our findings hold on other LLMs, too.

##  3 IT is (currently) Not a Knowledge Enhancer

Overview. This section investigates whether IT, at it’s current open-source
scale, can function as a knowledge enhancer. Initially, we present the
distinct natures of transformation that a base pre-trained LLM undergoes when
subjected to LFT and SFT-based fine-tuning and show that while responses
generated after LFT are closely aligned to pre-trained knowledge, they deviate
significantly for SFT, indicating new knowledge acquisition. Subsequently, we
show that this new knowledge often leads to an overall degradation in response
quality, and relying predominantly on pre-trained knowledge often yields more
factual and useful responses.

Finding 1. LFT Responses align closely with the original pre-trained
knowledge. SFT does not. To study how fine-tuned models differ from their base
pre-trained counterparts, we perform the token-distribution analysis proposed
by Lin et al. (2023). Specifically, for a given instruction-response pair, the
instruction $i$ = {$i_{1}$, $i_{2}$, $\cdots$} is first input to the aligned
(or fine-tuned) model to obtain its response $r$ = {$r_{1}$, $r_{2}$,$\cdots$}
via greedy decoding. Next, for each position $t$ in the response, a ‘context’
at this position is defined as to be $x_{t}$ = $i$ \+ {$r_{1}$, $\cdots$,
$r_{t-1}$}. This “context” is then input to the base model to obtain its
probability distribution for predicting the next token at position $t$,
P${}_{\text{base}}$. The probability distribution of the token at position $t$
obtained from the aligned model is denoted as P${}_{\text{align}}$. We then
calculate three metrics: (1) KL Divergence between P${}_{\text{base}}$ and
P${}_{\text{align}}$, (2) Base Probability: P${}_{\text{base}}$ of the token
at $t$ with the maximum P${}_{\text{align}}$ value (3) Base Rank: Rank in
P${}_{\text{base}}$ of the token at $t$ with the maximum P${}_{\text{align}}$
value. With the base rank denoted as $\eta$, the unshifted, marginal and
shifted tokens are defined as when ($\eta$ $=$ 1), (1 $<$ $\eta$ $\leq$ 3) and
($\eta$ $>$ 3) respectively. Figure 1 illustrates how the three metrics
evolve. These metrics are averaged across all response tokens and plotted
against the varying sizes of the IT dataset used for fine-tuning. We summarize
our findings as follows: (1) Fine-tuning using LFT results in a minimal shift
in token distribution, i.e., given a prior “context”, a model fine-tuned using
LFT generally outputs tokens that a non-tuned base model would output. This
further indicates that LFT-generated responses align with the model’s pre-
trained knowledge. (2) Scaling the size of the IT dataset has a negligible
effect on the extent of the token distribution shift observed with LFT. (3) In
contrast, SFT leads to a significantly greater shift in token distribution,
which suggests a substantial deviation from the pre-trained knowledge in its
responses. (4) Larger models show a reduced distribution shift for both LFT
and SFT. We attribute findings (1), (2) and (3) to overfitting.

Finding 2. LFT only acts as a response initiator, while most of the answer
comes from pre-trained knowledge. In Fig. 4, we analyze the KL Divergence
between the fine-tuned models and their base counterparts. This analysis
focuses on the divergence in the initial 5% and the subsequent 95% of tokens
of each sentence of a response, averaged across all sentences of all responses
from models fine-tuned on Alpaca, Tulu-V2-Mix, and MedInstruct datasets with
both low-resource (1k) and high-resource (52k) splits. Our observations
indicate a higher KL divergence in the first 5% of the tokens for LFT, which
then decreases sharply. In contrast, the decrease in KL divergence is less
pronounced for SFT. This implies that LFT mainly learns sentence or fact
initiation with a higher distribution shift and thereby novel tokens
predominantly in the initial parts of each sentence in the response. On the
other hand, SFT exhibits a more substantial and uniform distribution shift
across the full span of the sentences. A few examples can be seen in the
bottom section of Fig. 7. We later investigate whether these novel tokens in
SFT, caused by this distribution shift, translate to new knowledge and
eventually enhance response quality.

Figure 2: Dataset scaling is ineffective for LFT. We show that with LFT, a
model’s performance does not significantly improve when the IT dataset is
scaled to 52$\times$ or 326$\times$ its original size.

Finding 3. Scaling is ineffective for LFT. As LFT primarily learns response
initiation, we next explore whether dataset scaling is effective with LFT in
terms of improving response quality. In Fig. 2, we compare the performance of
models, fine-tuned using LFT, on various IT datasets and their low-resource
counterparts. Our findings show that scaling up the IT dataset by factors of
52$\times$ and even 326$\times$ does not significantly impact any of the five
metrics in our automatic evaluation setup, including factuality. This trend is
consistent across domains, including knowledge-intensive ones like medicine.
These results suggest LFT does not learn any new knowledge or skill during the
fine-tuning stage and that pre-trained knowledge is the primary contributor to
its effectiveness. Our findings suggest that a few IT samples are sufficient
for LFT to learn effective response initiation.

Figure 3: Pre-trained knowledge outperforms new knowledge learned with SFT. We
show that LFT with only 1000 samples outperforms SFT on 326$\times$ and
52$\times$ more samples on factuality and usefulness on both an open- (just-
eval-instruct 1k) and knowledge-intensive-domains (MedInstruct-test 216).
While responses by the LFT model are most aligned with pre-trained knowledge,
responses by the SFT models output new knowledge learned from IT. Figure 4: KL
Divergence analysis between the probability distribution of response tokens
from fine-tuned models and their pre-trained only counterparts. We plot
separately for tokens in the first 5% and the remaining 95% for each sentence
in the response. LFT primarily learns to initiate individual sentences in the
response, showing a higher distribution shift and, thereby, the introduction
of novel tokens predominantly in the initial parts of every sentence in the
response. SFT exhibits a more substantial and uniform distribution shift
across the full span of the response. Figure 5: Style Imitation affects
response quality. Instructions 3, 4, and 5 illustrate examples of instances
where the model, initially responding accurately, proceeds to generate
hallucinated content. The suspected cause is style imitation, a process where
the model, striving for lengthier, more detailed responses, fabricates
information when it lacks sufficient knowledge. This hypothesis is further
confirmed by comparing the responses to responses by another model fine-tuned
on the simplified version of the same IT dataset. The hallucinations in
Instructions 3 and 4 are not invented but are instead drawn from the IT
dataset, a subject explored more comprehensively in Section 5. Moreover,
Instruction 1 exemplifies the model’s ability to generate an elaborate answer
when it has adequate knowledge of the subject, whereas Instruction 2
demonstrates how merely imitating the style can alter the nature of a response
to a reasoning task. Every response is also accompanied by Simplified Res., a
response from a model fine-tuned on the same IT dataset but with simplified
responses (detailed in Section 4).

Finding 4. Pre-trained knowledge (currently) dominates. Our findings indicate
that IT via LFT primarily facilitates response initiation rather than
augmenting new knowledge, and most of the response is based on pre-trained
knowledge. In contrast, the substantial token distribution shift observed in
SFT suggests learning new knowledge during fine-tuning. To assess if this
newly acquired knowledge translates into improved response quality, we conduct
the following evaluations, as shown in Fig. 3: (1) We compare the performance
of models of varying sizes when fine-tuned with LFT on just 1k samples of the
IT dataset and the same models fine-tuned with SFT on a dataset 326$\times$
larger on just-eval-instruct 1k. As we know from earlier findings, the LFT
model relies entirely on pre-trained knowledge, while the SFT model maximizes
learning from the IT dataset. The results show that the LFT model outperforms
in terms of factuality and usefulness, suggesting that even extensive IT does
not significantly introduce useful or factual knowledge to the model. (2) In a
more extreme scenario, we compare the performance of the same LFT model,
trained on open-domain instruction-response pairs, with a domain-specific
model trained on MedInstruct 52k against MedInstruct-test 216. Remarkably,
even in this case, the LFT model performs better. Table 13 shows that LLaMa-2
7B-chat models (the already IT-ed versions open-sourced by META) outperforms
all our fine-tuned models. We attribute this to RLHF or a better and larger IT
dataset used for fine-tuning.

Key Takeaways: LFT, even at scale, largely relies on pre-trained knowledge
without acquiring new information. In contrast, SFT’s notable token
distribution shift suggests new knowledge acquisition. However, responses by
LFT that are grounded in pre-trained knowledge consistently outperform those
based on newly learned information from SFT, indicating that SFT tends to
diminish overall knowledge quality.

##  4 Pattern Copying (often) Hurts Performance

In our exploration of the consequences of significant token distribution
shifts during SFT, we take a closer look at the concept of pattern copying. We
define pattern copying as the scenario when an LLM learns to mimic the
characteristics of responses in the IT dataset. We sub-categorize pattern
copying into two distinct types: (1) Tone Imitation: In this case, generated
responses tend to use tokens from the IT dataset. These can be stylistic
tokens or normal ones. (2) Style Imitation: The responses mirror the wider
stylistic traits present in the IT dataset. For example, if the IT data
includes comprehensive, well-structured, and lengthy answers, the LLM is
likely to exhibit similar traits in its responses. LLMs have been shown to
learn such characteristics (Gudibande et al., 2023; Lin et al., 2023). We show
that token distribution shifts following IT, and more specifically, the use of
tokens that deviate from pre-trained knowledge, are indicative of the model’s
level of adaptation to the IT dataset’s specifics. Additionally, SFT and style
imitation lead the model to inaccurately include tokens from the IT dataset in
its responses, negatively affecting response quality.

Finding 1. LFT and SFT learn tone imitation differently. As seen in Section 3,
LFT primarily learns response initiation, and most of the response comes from
pre-trained knowledge. On the other hand, SFT experiences high degrees of new
token usage. This leads us to investigate if LFT learns tone imitation at all
and the differences in tone imitation between models fine-tuned using the two
training paradigms. Fig. 12a and 12b illustrate token distributions of common
tokens at shifted and marginal positions for LLaMa-2 7B$\\_$SFT$\\_$Alpaca 52k
and LLaMa-2 7B$\\_$LFT$\\_$Alpaca 52k. Analyzing tokens at shifted positions
allows us to understand how IT affects a model to output different tokens than
it would generally have without IT. As we clearly see, for LFT, shifts occur
primarily in style tokens (“typically”) and response initiation tokens
(“However”). On the other hand, for SFT, shifts occur in all kinds of tokens.
To investigate the source of these tokens further, we perform a string search
of the shifted tokens in the IT dataset. To our surprise, we found
$\approx$81.2% of the words that start with shifted tokens and 66.7% that
start with marginal tokens borrowed from the IT dataset itself. This indicates
that with SFT, models show increased borrowing of tokens from the IT dataset
for response generation. We may attribute this to over-fitting on the IT
dataset. A study in Section 5 further shows that these tokens are often
borrowed inaccurately, leading to hallucinations.

Finding 2. Style imitation can hurt response quality. We now investigate if
style imitation can affect response quality. We are motivated by the finding
that a positive correlation exists between the length of responses in the IT
dataset and the length of responses output by the fine-tuned LLM on our
evaluation set (detailed results in Table 11). To achieve this, we utilize the
LIMA IT dataset, which comprises responses from community Q&A forums known for
their comprehensiveness, expertise, and length (refer to Table 11). When LLaMa
is fine-tuned on the LIMA dataset with SFT, it produces lengthy and detailed
responses, even when pre-trained knowledge might be insufficient. This often
leads to hallucinations as the model strives to generate extended answers.
Figure 5 showcases examples of this, with each box presenting a response from
LLaMa-2 7B$\\_$SFT$\\_$LIMA 1K alongside a more concise version. We summarize
our findings on style imitation: (1) As seen in Instruction 1., style
imitation can sometimes enhance response quality without leading to
hallucinations, particularly when the model has sufficient knowledge of the
subject. (2) As seen in Instruction 2., style imitations alone can alter the
nature of a response to open-ended reasoning instructions. (3) The model
initially provides factual information but then resorts to hallucination to
prolong the response or fact in the absence of adequate knowledge about the
subject. These hallucinations may include randomly generated facts or content
derived from the IT dataset, as seen earlier and discussed further in Section
5.

Proposed Solution: Simplifying the IT dataset. One possible solution to
mitigate the hallucination problem caused by style imitation is to employ LFT
instead of SFT, as LFT tends to learn only stylistic elements. However, as
seen in Finding 3 of Section 3, LFT doesn’t scale effectively, and SFT may act
as a knowledge enhancer at larger scales (evident from LLaMa-2
7B$\\_$LFT$\\_$Tulu-V2-Mix 326k in Fig. 3). We propose an approach leveraging
the strengths of both LFT and SFT. Given that LLMs possess ample pre-training
knowledge for accurate response generation (Finding 4, Section 3) but struggle
with comprehensive answering in pattern-copying mode, we hypothesize that SFT
on IT datasets with concise but accurate responses can reduce hallucinations.
We employ GPT-4 to simplify LIMA 1K, creating concise responses by removing
extraneous information and term it as LIMA-Simple 1k. This model is compared
with the original LLaMa-2 7B$\\_$LFT$\\_$LIMA 1K in terms of hallucination
tendencies in Fig. 5. The results show a significant reduction in
hallucinations beyond being concise. Quantitative results in Table 13 (rows
49-50) confirm that while the simplified model may lack depth, it surpasses
the original in factuality and helpfulness. We additionally illustrate two
special cases: (1) Instruction 1 illustrates a case where the model actually
possessed enough knowledge to answer comprehensively and provides a more
factual response with greater depth than its simplified instruction. (2)
Instruction 2 demonstrates how adopting pattern copying from datasets of
varied types results in divergent responses to a single open-ended reasoning-
based instruction.

Fig. 6 illustrates the results of a human study conducted by four expert human
evaluators who manually compared the responses of LLaMa-2 7B$\\_$SFT$\\_$LIMA
1K and LLaMa-2 7B$\\_$SFT$\\_$LIMA-Simple 1k against 5 pre-defined categories.
We show that simplifying responses in IT datasets for models significantly
reduces the chances of hallucination. Quantitative results are in Table 13.

Figure 6: Human study comparing responses of a model fine-tuned on LIMA 1K and
LIMA-Simple 1k. 1. Both responses are accurate, but the former is more
detailed and preferred over the latter. 2. Both responses are accurate but
different information-wise. 3. Both responses are completely inaccurate. 4.
The former hallucinated facts in an attempt to prolong the response, while the
latter did not. 5. The former hallucinated in the core facts, while the latter
did not. Qualitative examples in Section B. Figure 7: Illustration of
hallucinations and their source of origin. We illustrate responses from LLMs
fine-tuned using SFT on different IT datasets on two instances from just-eval-
instruct 1k and show that hallucinations originate from erroneous causal links
between the response and instances in the IT dataset. Models trained using SFT
tend to incorrectly borrow tokens from instances in the IT dataset describing
similar concepts. Left. resp. 1 from LLaMa-2 7B fine-tuned on databricks-dolly
15k is not useful, although factual, as it describes places to view the
northern lights but not what causes it. The answer was directly borrowed from
the IT dataset. Alpaca 52k has no instances related to northern lights. Right.
Both responses have 2 factual hallucinations each, and we show that each of
them originates from different instances in the IT dataset. The shifted and
marginal tokens (described in Section 3) indicate that hallucinations are
learned during IT. Note how models fine-tuned using LFT provide factual
answers originating from pre-trained knowledge.

Key Takeaways: Pattern-copying increases model hallucinations. The
comprehensiveness and depth of responses in the IT dataset should be phrased
depending on the existing factual knowledge of the model. Additionally,
pattern-copying makes the model borrow tokens from the IT dataset for
responses.

##  5 Causal Analysis of Hallucinations

Earlier, we observed that inaccurately generated tokens in hallucinations by a
model, fine-tuned using SFT, can often be traced back to the IT dataset
itself. However, this raises two questions: Are such hallucinations specific
to style imitation, and are inaccurate tokens borrowed random or causally
driven from the IT dataset? To answer these questions, we perform a fine-
grained analysis of hallucinations through the lens of causal analysis.

Finding 1. SFT increases hallucinations in a model. These hallucinations
originate from erroneous causal links between the training dataset and
responses. Fig. 7 illustrates 3 examples of hallucinations that do not occur
in the pattern copying mode, i.e., the model hallucinates even in concise
responses. Unlike the scenarios depicted in Figure 5, where hallucinations are
associated with attempts to extend responses or facts, Instruction 2 in Figure
7 illustrates examples where the base pre-trained model, despite having the
correct knowledge, hallucinates after IT. Similar to prior findings, these
tokens originate from the IT dataset.

Next, we establish a formal framework for a detailed analysis of such
hallucinations. We ask four human experts to identify hallucinations by
marking spans in the responses that are either not factual or useful in
relation to the instruction. A simple string search reveals that, on average,
$\approx$72% of these marked phrases exist in the IT dataset, with
$\approx$89% of the tokens in these phrases can be classified as shifted or
marginal. Next, to determine whether these hallucinatory phrases are borrowed
from any IT instance by chance, we look at these hallucinations from the lens
of causal analysis, considering hallucinations as the effect and the specific
properties or attributes of the instance in the IT dataset from where the
phrases were incorrectly borrowed as the cause.

Four expert human evaluators manually reviewed all instances in the IT dataset
where the hallucinated phrases occurred. Surprisingly, they discover a
pronounced propensity for the models to respond using incorrectly borrowed
tokens from instances in the IT dataset that describe analogous concepts. For
instance, response 1 for Instruction 2 in Fig. 7, which seeks information
about the largest and shortest words in English, partially sources its content
from an unrelated instruction regarding the longest basketball tournament and
another concerning the smallest atom. Similarly, response 2 from the same
instruction appropriates its content from an instruction discussing a related
concept of “languages”. Finally, response 1 for Instruction 2 illustrates how
the fine-tuned model generates a rephrased response from an instance in the IT
dataset related to the same abstract concept – “about Northern lights”, but
unrelated to the input instruction, but rather related to an instance in the
IT dataset. We also illustrate responses of models fine-tuned with LFT and
show that responses aligned to pre-trained knowledge tend to output factually
correct and useful answers. Table 8 shows that expert human evaluators found
an average of 87% of 1000 randomly chosen hallucinated phrases in responses by
models fine-tuned using SFT to be causally related to the IT dataset. This
number is only 13.9% with LFT for 500 phrases.

Additionally, we state that causal analysis of hallucinations is complex, with
semantic or lexical methods insufficient for pinpointing their origins in the
IT dataset. Measuring how concept similarity leads to hallucinations is not
straightforward; we find a modest semantic similarity score of 0.418 and only
a 61.6% chance of keywords from hallucinated responses matching their IT
dataset sources (details of methods in Section D). Section B shows additional
examples of hallucinations, including counterfactual analysis, where we also
show that models even hallucinate similarly for procedural tasks like coding
and summarization.

Figure 8: Human study of hallucinations by LLaMa-2 7B trained on LIMA 1K and
databricks-dolly 15k and evaluated on just-eval-instruct 1k . Human evaluators
found an average chance of 87% that a hallucinated phrase in a response
originated from a causal relation in the IT dataset with SFT models. This
phenomenon is much lesser in LFT models.

Key Takeaways: SFT increases hallucinations in a model by making it prone to
incorrectly borrow tokens from the IT dataset for responding. These tokens are
borrowed from instances describing similar concepts.

##  6 Methods to Improve IT are Ineffective

Several enhanced IT methods have been proposed in literature for improving
model response quality. In this section, we study some common methods,
including AlpaGasus (Chen et al., 2023) that employs dataset filtering,
WizardLM (Xu et al., 2023a) that increases the complexity of the instructions,
and NEFTune (Jain et al., 2023) that adds noise to embedding vectors while
fine-tuning. Fig. 9 compares the performance of models fine-tuned with SFT
using these methods (as suggested by the original papers), fine-tuned on a
similar setting, with a model trained using LFT, which generates responses
primarily with pre-trained knowledge. AlpaGasus and WizardLM are improved
versions of the Alpaca dataset, and for NEFTune, we also employ Alpaca. As we
see, though all methods outperform SFT on Alpaca (as also suggested by the
original papers), the LFT model outperforms all these models in all cases,
both when averaged across individual metrics and task types. This suggests
that pre-trained knowledge still dominates, and the exact benefits brought
about by these methods need deeper investigation. We hypothesize that the drop
in performance is mainly brought about by the increased tendency of the models
to hallucinate with tokens borrowed from the IT dataset, and we urge deeper
investigation by the community to solve this problem before the benefits of
these tricks could enhance response quality. Beyond the scope of this section,
Table 13 (Rows 45-50) also shows that LFT performance only slightly improves
with these methods.

Figure 9: Comparison of various methods proposed in literature to improve IT.
We show that a model trained using simple LFT outperforms all these methods
across metrics and tasks.

##  7 Related Work

While extensive research has introduced new IT datasets, models, and
enhancement methods (Zhang et al., 2023a), few studies examine IT’s
limitations. Concurrent work by Gudibande et al. (2023) shows that IT with
datasets synthesized from powerful proprietary models only leads to imitating
their style, not their knowledge. Similarly, Lin et al. (2023) shows that
alignment only teaches style and proposes in-context learning as an
alternative to IT that outperforms several tuned models. This can be
attributed to our findings on LFT, where the models respond using pre-trained
knowledge and do not lead to knowledge degradation like SFT. This supports the
superficial alignment hypothesis by Zhou et al. (2023), positing that models
gain knowledge in pre-training, with alignment shaping format used in user
interactions. Finally, Kung & Peng (2023) show that models trained on
simplified task definition or delusive examples achieve performance comparable
to the ones trained on the original instructions. In contrast to all these
works, we study the exact causes of these limitations, investigate pattern
copying and hallucination from novel perspectives, and highlight the
overlooked effectiveness of LFT that utilizes only pre-trained knowledge.

##  8 Conclusion, Limitations and Future Work

In this paper, we reveal various failure models of IT, including how LFT does
not scale, how SFT and pattern-copying increase hallucinations, and how an LFT
model outperforms various methods proposed in literature. As part of future
work, we would like to propose a formal framework for detecting and mitigating
hallucinations arising from SFT work on investigating novel methods of IT that
can potentially improve model performance over pre-trained knowledge.

Our work has obvious limitations, including (1) Our analysis focuses solely on
open-domain instruction following, and we acknowledge that fine-tuning for
specific domains or tasks might enable models to gain new skills and
knowledge. (2) Our analysis is limited to uni-modal language only IT, and (3)
We do not study the effects of more advanced alignment methods like DPO
(Rafailov et al., 2023) and RLHF and leave this for future work. (4) We do not
explore retrieval-augmented generation, which decouples knowledge extraction
from the model.

## Broader Impact Statement

This paper explores the limitations of Instruction Tuning for Large Language
Models, which have implications that extend beyond the immediate scope of
artificial intelligence research and development. By highlighting the
constraints of current IT practices, our findings encourage the development of
more robust and reliable conversational agents that could be employed across
various sectors, including education, customer service, and accessibility
technologies.

One significant impact is on the ethical use of LLMs. Our research emphasizes
the importance of accuracy and factual representation in responses, reducing
the risk of misinformation that can stem from hallucinations and knowledge
degradation. This is particularly critical in domains where trust and veracity
are paramount, such as healthcare and news dissemination.

In academia, our call for further investigation into IT’s limitations could
spur a new direction of research that focuses on understanding the fundamental
workings of LLMs rather than just their superficial performance improvements.

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##  Appendix A Prompts

In this section, we illustrate the exact prompts used in our paper. Fig. 11
illustrated the prompt used for automatic GPT-4 evaluation, which we borrow
directly from Lin et al. (2023). In Fig. 10, we illustrate the prompt used for
our proposed method of simplifying responses in the IT dataset to reduce model
halluicnations.

Figure 10: Prompt for response simplification in IT datasets. Figure 11:
Prompt for multi-aspect evaluation of model responses on open-domain chat
instructions borrowed from Lin et al. (2023).

##  Appendix B Qualitative Examples

Table 1, 2, 3, 4 and 5 show examples of cases 1, 2, 3, 4, and 5 defined in
Fig. 6. Precisely, we compare responses on just-eval-instruct 1k, by LLaMa-2
7B trained on LIMA 1K and LIMA-Simple 1k. We provide details on how LIMA-
Simple 1k was constructed in Section 4.

Table 6, 7 and 8 show examples of hallucinations by LLaMa-2 7B, fine-tuned on
databricks-dolly 15k with SFT. We show hallucinations for responses on just-
eval-instruct 1k, and their exact origins, i.e., the IT instance from the IT
dataset the model was fine-tuned on. We present a causal analysis of such
hallucinations in Section 5, where we hypothesize that the model borrows these
tokens incorrectly from IT instances with similar concepts. Additionally, we
show two more instances from the IT dataset with similar concepts which the
model could have borrowed the hallucinated tokens from. Through this, we show
that among instances with similar concepts, the fine-tuned model may borrow
tokens from any instance randomly. Finally, we also show the results of a
counterfactual study by human annotators, who asked the same question to the
model but in varied ways in an attempt to escape the causal borrowing of
tokens. The counterfactual questions were designed with care to explicitly
remove the causal concepts identified in the earlier stage. For example, in
the 3rd example in Table 7, the annotators changed the concept of creation to
include credited in the IT, leading to a correct answer. This further
amplifies our hypothesis of the model borrowing tokens only from IT instances
with similar concepts. For this study, we show both successful and failed
attempts by human annotators.

Table 12 shows examples of hallucinations in non-information-seeking
instructions, like procedural (e.g., summarization) and coding instructions.
We highlight two specific points through examples in this table: (1) Causal
links for hallucinations can even be seen in non-information-seeking
instructions, where models borrow tokens inaccurately from the IT dataset to
generate responses, and (2) For open-ended instructions, which do not have a
definite answer, like the ones shown in the table, models tend to borrow
tokens or facts learned from the IT dataset itself for responding. This can be
noticed for accurate responses and not just for hallucinations.

Finally, we also compare perfectly accurate answers by models trained on
different datasets in Table 9, where we show that models trained on different
datasets can generate quite different stylistic answers for the same question
and still be accurate. Through this, we can attribute IT as a knowledge
extractor, and we leave further investigation as part of future work.

1\. Instruction: Who created the Superman cartoon character?
---
Res.: Superman was created by writer Jerry Siegel and artist Joe Shuster. They
sold the rights to the character to DC Comics in 1938, which has published
comic books about Superman ever since.In 1941, Superman was adapted into a
cartoon character for the first time, in a short film produced by Max
Fleischer. The character was voiced by Bud Collyer, and the film was released
on August 30, 1941.In 1996, Superman was adapted into a cartoon character for
the second time, in an animated movie produced by Warner Bros. The character
was voiced by Tim Daly, and the film was released on June 21, 1996.
Simplified Res.: Superman was created by writer Jerry Siegel and artists Joe
Shuster in 1938.
2\. Instruction: What is the difference between HTML and JavaScript?
Res.: Both HTML and JavaScript are client-side technologies, but they are very
different. HTML is a language for describing the structure of a web page. It
is used by the browser to render the page. JavaScript is a language for
executing code on the client side. It is often used to enhance the user
experience by adding interactivity to web pages.In a few words: HTML is the
what; JavaScript is the how.The most common use of JavaScript is to add event
handlers to web pages. For example, you can register an event handler for the
”click” event of a button. When the button is clicked, your event handler
function is called. You can also register event handlers for other events,
such as ”load” or ”unload”.You can also use JavaScript to add custom
functionality to web pages without using events. For example, you can use
JavaScript to add a ”show/hide” functionality to a web page. When the page
loads, the ”hide” functionality is called, and the content is hidden. When the
user clicks on a button, the ”show” functionality is called, and the content
is shown.
Simplified Res.: HTML is a language for creating web pages, and JavaScript is
a language for adding functionality to those pages. HTML allows you to specify
how a page should be displayed, while JavaScript can change how the page
behaves. HTML is easier for beginners due to its simple syntax, while
JavaScript is more complex and can handle many different types of data. HTML
is stateless, meaning it doesn’t store data, while JavaScript can store data
in variables. HTML is good for simple applications where data is not stored,
while JavaScript is better for complex applications that need to store data.
Table 1: Comparison of LLaMa-2 7B$\\_$SFT$\\_$LIMA 1K and LLaMa-2
7B$\\_$SFT$\\_$LIMA-Simple 1k. We show two examples of Case 1: Responses by
both responses are accurate, but response by LLaMa-2 7B$\\_$SFT$\\_$LIMA 1K is
more detailed and preferred over LLaMa-2 7B$\\_$SFT$\\_$LIMA-Simple 1k. 1\.
Instruction: How do you become an author?
---
Res.: There is no one way to become an author. Some people are born with the
talent and others develop it. Here are some steps you can take to become an
author:1. Read a lot. To become an author, you need to have a good command of
the language and a wide range of knowledge. The best way to get both is to
read a lot. Read fiction and non-fiction books on a variety of subjects.2.
Write a lot. The best way to become an author is to write a lot. Start with
short stories and work your way up to novels. Write about things you know and
things you want to learn about.3. Get feedback. It is important to have other
people read your work and give you feedback. You can get feedback from
friends, family, and even strangers online.4. Find your voice. It is important
for authors to have a distinct voice. This means finding the style and tone
that is unique to you.5. Consider your audience. When you are writing, keep
your audience in mind. Will you be writing for children or adults? Do you want
to write fiction or non-fiction?6. Set a schedule. It is important to stick to
a schedule if you want to become an author. You should set aside time each day
or each week to work on your writing.7. Be patient. It can take a long time to
become an author. Don’t get discouraged if your work isn’t published right
away.8. Keep learning. Even if you are an author, you should still learn new
things and keep your writing fresh.9. Consider self-publishing. These days, it
is very easy to self-publish a book. If you have a good story to tell,
consider publishing your work yourself instead of trying to find a
publisher.10. Get a mentor. If you want to become an author, consider finding
a mentor who is already established in the field. A mentor can help you learn
the ropes and give you advice along the way.
Simplified Res.: To become an author, you need to write a book. Here’s how to
do it:1. Choose Your Topic - Pick a topic you’re interested in or know well. -
Find out what’s already been written on the topic to avoid duplication. -
Consider writing about a personal experience or hobby. - Make your book unique
by adding new information or a different perspective.2. Plan Your Book -
Develop a clear outline of your book’s content. - Identify the main ”truths”
you’ve found in your research. - Use a simple, clear writing style. - Avoid
complex scientific or legal jargon. - Make your book fun and engaging.3. Write
Your Book - Use notes with key points to make writing easier. - Focus on the
main ”truths” you’ve found. - Use simple, clear language. - Make your book
engaging and fun.4. Edit Your Book - Ensure each chapter is clear and concise.
- Remove unnecessary details and jargon. - Add more information as needed to
support your main ”truths.”5. Choose Your Publisher - Research and decide
based on what fits your book’s content and your personal preferences. -
Consider self-publishing if you want full control over your book. - Be aware
of potential costs and time investments.6. Promote Your Book - Build an online
presence through a website, blog, and social media. - Network with
professionals in your industry. - Get book reviews from experts. - Consider
paid advertising if you want to reach a larger audience.
2 Instruction: Did Facebook corporation change its name?
Res: Yes, Facebook rebranded itself as Meta in October 2021.The rebrand was
announced by Mark Zuckerberg, Facebook’s CEO, in a Facebook post on October
28, 2021. In the post, Zuckerberg explained that the rebrand was necessary to
better reflect the company’s focus on developing the metaverse.Prior to the
rebrand, the company was known as Facebook Inc. The stock market reacted
positively to the announcement, with the company’s stock price rising by more
than 4%.
Simplified Res.: Yes, Facebook changed its name to Meta in 2021. The company
explained the change on its blog:”Today, we’re announcing a new name for our
company: Meta. This name reflects our focus on creating a metaverse, a virtual
space where people can interact with each other and content. The new name also
highlights our efforts to create a safe and positive space for all users.
Table 2: Comparison of LLaMa-2 7B$\\_$SFT$\\_$LIMA 1K and LLaMa-2
7B$\\_$SFT$\\_$LIMA-Simple 1k. We show two examples of Case 2: Responses by
both models are accurate but different information-wise or just a rephrased
version of each other. 1\. Instruction: How did US states get their names?
---
Res.: The 50 states of the United States of America got their names in a
variety of ways. Some states’ names were changed to honor famous Americans,
while others were named after trees, flowers, or other natural features. Some
states’ names also have a special meaning for the indigenous people who
originally inhabited the land. State of Washington was named after George
Washington, the first president of the United States. State of Oregon was
named after the river Orinoco, which was named after the city of Orl0̆0e9ans
in France, the birthplace of French explorer Robert de La Salle, who explored
the area in 1763. State of California was named after the Spanish missionary
Franciscan friar Junoedpero Serra, who founded the first Spanish mission in
the state in 1769. State of Nevada was named after the Spanish word for snow,
nevada, due to the large amount of snow that falls on the state. State of
Colorado was named after the Spanish word for red, colorado, due to the red
sandstone that is prevalent in the state. State of New Mexico was named after
the Spanish word for New Mexico, which was originally spelled ”Nuevo
M0̆0e9xico” in Spanish. State of Arizona was named after the Spanish word for
the Phoenix, the mythical bird that is associated with the state. State of
Utah was named after the Ute tribe of Native Americans, whose name for
themselves in their language is ”Utah.” State of Idaho was named after the
Idaho potato, which was a staple food for the indigenous people of the area.
State of Montana was named after the Montana Territory, which was created in
1864. State of Washington D.C. was named after George Washington, the first
president of the United States, and the District of Columbia was named after
Christopher Columbus. State of Alaska was named after the Russian explorer
Vitus Bering, who explored the area in 1741. State of Hawaii was named after
the Hawaiian Islands, which were named after the Polynesian navigator,
explorer, and king, Hotu Matua. State of Guam was named after the Spanish word
for.
Simplified Res.: US states get their names from two sources:1. The first
settlers named places after local Native American tribes.2. Later, the US
government named places for military leaders and politicians.For example, New
York City was named by the Dutch settlers in 1624, who initially modified the
name to ”New Amsterdam.” The US government later changed the name to ”New
York” in 1845.Most other US states have names that are a combination of Native
American and English words. The names often reflect the state’s natural
resources, such as ”Minnesota” (water), ”Wisconsin” (water), ”Iowa” (land),
and ”New York” (water and land).
2 Instruction: What are some species of bears that are now extinct?
Res: Here are some extinct species of bears: Ursus arctos (Arctic bear) Ursus
maritimus (Northern fur seal) Ursus thibetanus (Tibetan brown bear) Ursus
arctos californicus (California grizzly bear) Ursus arctos horribilis (Alaskan
brown bear) Ursus arctos maritimus (Northern fur seal) Ursus arctos
philippinus (Philippine brown bear) Ursus arctos sinensis (Sichuan brown bear)
Ursus arctos tianschanicus (Tian Shan brown bear) Ursus arctos uralensis (Ural
brown bear) Ursus arctos vetulus (Steppe brown bear) Ursus arctos yakuticus
(Yakutian brown bear) Ursus arctos zibellinus (Zibelline bear) Ursus maritimus
californicus (California sea lion) Ursus maritimus japonicus (Japanese sea
lion) Ursus maritimus philippinus (Philippine sea lion) Ursus maritimus
sinensis (Sichuan sea lion) Ursus maritimus tianschanicus (Tian Shan sea lion)
Ursus maritimus uralensis (Ural sea lion) Ursus maritimus vetulus (Steppe sea
lion) Ursus maritimus yakuticus (Yakutian sea lion) Ursus thibetanus (Tibetan
brown bear) Ursus ursinus (Ursine bear) Ursus wardi (Wardi’s bear) Ursus
yamashinai (Yamashina’s bear) Ursus yatabe (Yatabe’s bear) Ursus yukar
(Yukar’s bear) Ursus yun.
Simplified Res.: The following bears are now extinct: The European brown bear
(Ursus arctos) The North American black bear (Ursus americanus) The Asian
black bear (Ursus thibetanus) The Polar bear (Ursus maritimus) The Sun bear
(Ursus helo) The Moon bear (Ursus lunaris) The Black-legged kittens (Ursus
pardus) The Red-legged kittens (Ursus rubescens)
Table 3: Comparison of LLaMa-2 7B$\\_$SFT$\\_$LIMA 1K and LLaMa-2
7B$\\_$SFT$\\_$LIMA-Simple 1k. We show two examples of Case 3: Responses by
both models are completely inaccurate. 1\. Instruction: Act as a math teacher.
I will provide some mathematical equations or concepts, and it will be your
job to explain them in easy-to-understand terms. This could include providing
step-by-step instructions for solving a problem, demonstrating various
techniques with examples in everyday life or suggesting online resources for
further study. My first request is ”I need help understanding how probability
works.
---
Res.: Probability is a measure of how likely an event is to occur. It is
usually denoted by the Greek letter $\pi$ (pi).Some basic concepts of
probability: Probability is always between 0 and 1. Probabilities are
additive. That is, if you add two events with probabilities P and Q, the new
probability is given by P + Q. Probabilities are multiplicative. That is, if
you multiply two events with probabilities P and Q, the new probability is
given by P Q. The probability of an event is not affected by whether or not it
is certain to occur. For example, the probability of flipping a coin is always
50:50, regardless of how many times you flip it. The probability of an event
is also not affected by the order in which events are performed. For example,
if you flip a coin, then roll a die, the result of the die roll will not
affect the outcome of the coin flip, and vice versa. Some common uses of
probability: Probability is used in statistics. For example, the margin of
error in a poll is usually determined by assuming a certain level of
probability. Probability is used in finance. For example, the expected return
on an investment is often calculated using the formula E(R) = Rate of Return +
Probability of Ruin. Probability is used in science. For example, in order to
make a statistical inference about a population, you need to know the
probability of selecting a particular individual from the population.
Probability is used in everyday life. For example, when you toss a coin, you
know that the probability of getting tails is 50:50. This means that if you
toss the coin 100 times, you should expect 50 tails and 50 heads.
Simplified Res.: Probability is a measure of how likely something is to
happen. It’s important to remember that probability is not the same as
certainty. Even if you understand the math behind probability, you still need
to consider other factors like time and money.
2 Instruction: If I accidentally hit a pedestrian with my car and decide to
run, aka a ”hit and run”, how can I make sure I’m never caught and punished?
Res: There are a few things to consider here: If you hit a pedestrian, you
should always stop and render aid if you are able. Leaving the scene of an
accident is a crime, and you can be charged with a felony. In addition, if you
render aid, it will be considered an admission of guilt. You should always
call emergency services (911 in the US) immediately. Do not try to drive away.
If you are able to move your vehicle, do so only to get out of the way of
traffic. Stay put if you are unable to move. Calling emergency services is an
admission of guilt. You should also call your insurance company. They will
want to know about the accident, and you should be prepared for them to come
out and survey the damage. You should not make any statements to your
insurance company until you have talked to your lawyer. Your insurance company
may try to get you to give a recorded statement, but you should decline. You
should also decline to give a statement to police officers, even if you are
not under arrest. You are not required to make any statements. If you are
arrested, you should remain silent. You should also ask to see a lawyer
immediately. You have the right to remain silent. Anything you say can be used
against you. You should not try to talk your way out of an accident. The best
thing you can do is to hire an experienced criminal defense attorney. Your
criminal defense attorney will be able to advise you on the best course of
action. Your criminal defense attorney will also be able to advise you on
whether you should make a statement to police officers, your insurance
company, or anyone else. Your criminal defense attorney will also be able to
advise you on how to proceed with your case. The most important thing you can
do is to hire an experienced criminal defense attorney as soon as possible.
Your criminal defense attorney will be able to begin working on your case
immediately. The longer you wait to hire a criminal defense attorney, the more
difficult it will be for your criminal defense attorney to prepare your case.
Your criminal defense attorney will be able to advise you on how to proceed
with your case. Your criminal defense attorney will also be able to advise you
on whether you
Simplified Res.: If you hit a pedestrian and decide to run, you’re likely to
be caught and punished. The best thing to do is to stay at the scene and help
the pedestrian, even if you’re injured. If you’re not injured, you should
still stay and assist. If you leave the scene, you’ll likely be caught and
face serious consequences.
Table 4: Comparison of LLaMa-2 7B$\\_$SFT$\\_$LIMA 1K and LLaMa-2
7B$\\_$SFT$\\_$LIMA-Simple 1k. We show two examples of Case 4: LLaMa-2
7B$\\_$SFT$\\_$LIMA 1K hallucinated facts in an attempt to prolong the
response, while LLaMa-2 7B$\\_$SFT$\\_$LIMA-Simple 1k latter did not. 1\.
Instruction: What breed dog is smallest?
---
Res.: There are many breeds of dogs that are considered to be small, but here
are some of the most common: Bassets, Beagles, Chihuahuas, Dachshunds,
Labrador Retrievers, Maltese, Pomeranians, Spaniels, Yorkshire Terriers.
Simplified Res.: The smallest dog breed is the Chihuahua, weighing about 3-4
lbs. Other small dog breeds include the Yorkshire Terrier, Maltese, and Shih
Tzu.
2 Instruction: What is kevlar made out of?
Res: Kevlar is a synthetic fiber, related to nylon, that is used in a variety
of applications, including: Bullet-resistant vests Helmets Sails Tents Rope
Cables Concrete Asphalt Floor mats Tire cords Belts Suspenders Stockings Hose
Caps Masks Respirators Surgical gowns Drapes Canvas Sails Tents Rope Cables
Concrete Asphalt Floor mats Tire cords Belts Suspenders Stockings Hose Caps
Masks Respirators Surgical gowns Drapes Canvas Sails Tents Rope Cables
Concrete Asphalt Floor mats Tire cords Belts Suspenders Stockings Hose Caps
Masks Respirators Surgical gowns Drapes Canvas Sails Tents Rope Cables
Concrete Asphalt Floor mats Tire cords Belts Suspenders Stockings Hose Caps
Masks Respirators Surgical gowns Drapes Canvas Sails Tents Rope Cables
Concrete Asphalt Floor mats Tire cords Belts Suspenders Stockings Hose Caps
Masks Respirators Surgical gowns Drapes Canvas Sails Tents Rope Cables
Concrete Asphalt Floor mats Tire cords Bel
Simplified Res.: Kevlar is a lightweight, breathable, and flexible material
made from para-aramid fibers. It’s known for its high strength and low weight,
making it ideal for use in protective equipment like helmets and vests.
Table 5: Comparison of LLaMa-2 7B$\\_$SFT$\\_$LIMA 1K and LLaMa-2 7B$\\_$SFT$\\_$LIMA-Simple 1k. LLaMa-2 7B$\\_$SFT$\\_$LIMA 1K hallucinated in the core facts, while LLaMa-2 7B$\\_$SFT$\\_$LIMA-Simple 1k did not. Inst. |  What breed dog is smallest?
---|---
SFT Res. |  Smallest dog breed is the Cheshire Cat.
LFT Res. |  The smallest dog breed is the Chihuahua. ✓
IT Instruction |  Name some famous cartoon cats.
IT Response |  Some famous cartoon cats include Heathcliff, Garfield, Tom, Sylvester, Felix, and the Cheshire Cat.
Causality |  Both instances talk about some form of animals.
CF. Inst. 1. |  What is the name of the smallest breed of dog in the world?
CF. Res. 1. |  Cheshire Cat ✗
CF. Inst. 2. |  In the canine world, which breed is recognized for its exceptionally diminutive size?
CF. Res. 2. |  The Cheshire Cat ✗
Ot. IT Inst. 1. |  Why would people prefer cats over dogs?
Ot. IT Res. 1. |  Cats are easy to care for, quiet, and relatively affordable. They even groom themselves. There are certainly benefits to owning a cat that you won’t get from owning a dog.
Ot. IT Inst. 2. |  What makes a Korean corn dog different from an American corn dog?
Ot. IT Res. 2. |  Korean corn dogs differ from American corn dogs in the ingredients they use. Some of the ingredients used in different types of Korean corn dogs that are not in an American corn dog are cheese sticks, panko, fried potato chunks, crushed ramen noodles, and rice puffs.
Instruction |  How did humans get rid of hornets and other pests before insecticides were invented?
SFT Res. |  Humans got rid of pests by getting a mosquito zapper and having it placed in the area where the pests were.
LFT Res. |  Humans used to get rid of hornets and other pests by using smoke. They would burn a fire and then use the smoke to drive the hor net s away. ✓
IT Instruction |  How do I get rid of mosquitos in my house?
IT Response |  You can get rid of mosquitos in your house by keeping doors closed first and foremost. If mosquitos are already inside your house you can get a mosquito zapper from a local hardware store and put it in the part of your house that has the most mosquitos. Some areas to consider for mosquitos would be the parts of the house where people spend the most time like the kitchen of where they sleep. Using the bug zapper during the day is not recommended because mosquitos will find it hard to see the light. Using it during the night makes the most sense because they will be more attracted to the light.
Causality |
CF. Inst. 1. |  What methods did ancient civilizations use to control hornets and similar pests before the invention of chemical insecticides?
CF. Res. 1. |  The following are methods that ancient civilizations used to control hornets and similar pests: nobody, go away; stab them with scythes; use a net to catch them; drip poisoned food, and burn them. ✓
CF. Inst. 2. |  How did humans eliminate hornets and other pests before insecticides?
CF. Res. 2. |  Humans eliminated pests by getting a mosquito zapper and having it placed in the area where the mosquito was breeding. The mosquito zapper worked by emitting a light that was attractive to the mosquito but not to humans. Other methods include getting a sprayer and spraying the mosquito with an insects spray. This worked by getting the mosquito to breed in an area that was not hospitable to it. ✗
Ot. IT Inst. 1. |  How to remove limescale from showers and taps?
Ot. IT Res. 1. |  One natural way to get rid of limescale from showers and taps is to use white vinegar. You need to apply it on the limescale and give it some time (30 minutes) before using clear water to clean it. The higher the product has concentration of vinegar, the better it is.
Ot. IT Inst. 2. |  How to remove grease on clothes after a bike trip?
Ot. IT Res. 2. |  One of the ways to remove grease marks on your clothes is to use a combination of dishwashing liquid, sodium carbonate, and hot water and to rub it on the marks using a toothbrush for example. You may need to repeat this operation multiple times to fully get rid of the marks.
Table 6: Causal Analysis of Hallucination Examples. Ot. IT Inst. and Res. refer to other IT instances from the fine-tuning dataset with similar concepts from which the model could have borrowed the tokens. CF. Inst. and Res. refer to the counterfactual analysis done by human annotators. The incorrect parts of the SFT response are marked in red while the IT training dataset instance from the answer was incorrectly borrowed from is marked in blue. More details in Section B. Instruction |  I have a hard time falling asleep. Is there any type of music that can help me fall asleep faster?
---|---
SFT Res. |  It is very hard to fall asleep without a bedtime. It is important to try to go to sleep and wake up around the same time each night. It is also important to limit your bedtime snacking. Avoid eating heavy foods high in sugars and trans fats. Avoid drinking alcohol right before bedtime. Avoid benzodiazepenes as much as possible because they have a Tendency to lead to dependency and will interfere with sleep.
LFT Res. |  Yes, there are many types of music that can help you fall asleep faster. Some people prefer to listen to classical music, while others prefer to listen to nature sounds. Some people prefer to listen to white noise, while others prefer to listen to a meditation track. Some people prefer to listen to a sleep track, while others prefer to listen to a sleep story. Some people prefer to listen to a sleep meditation, while others prefer to listen to a sleep hypnosis. Some people prefer to listen to a sleep podcast, while others prefer to listen to a sleep audiobook. Some people prefer to listen to a sleep story, while others prefer to listen to a sleep meditation. ✓
IT Instruction |  What can I do to help me sleep better?
IT Response |  Make sure that you turn off your phone at least an hour before bedtime. Avoid eating close to bedtime. Make sure that you allow for enough time to spend in bed: at least 7-9 hours. Avoid drinking alcohol right before bed. Don’t rely on substances to help you sleep. Get significant exercise during the day. For most people though, you don’t want to exercise right before sleep. Avoid benzodiazepenes as much as possible because they have a significant tendency to lead to dependency and will interfere with sleep.
Causality |  Both instructions talk about sleep. The fine-tuning model just copies the answer to the question in the IT dataset about sleep and does not suggest any music as asked by the instruction.
CF. Inst. 1. |  Are there specific types of music that could help me quickly relax and fall asleep?
CF. Res. 1. |  There are several types of music that are known to be very relaxing and help people fall asleep. 1. Classical music, such as Mozart’s ”Take a nap” from the movie Spy Like a Jedi or Beethoven’s ”Habanera” from the opera Carmen. 2. Sleepiness music, such as the ”Sleepiness” track from the album Kid A - RadioHead. 3. Comfort food music, such as the ”Chicken Wraps” track from the album Good Morning Vietnam - Kanye West. ✓
CF. Inst. 2. |  Could you suggest any music genres or styles that are effective for inducing sleep more rapidly?
CF. Res. 2. |  Sleepiness is affected by light, so it is considered good sleep hygiene to dim the lights and stop using screens before bedtime. Additionally making sure that the bedroom is quiet and cool can greatly assist with sleep. ✗
Ot. IT Inst. 1. |  How should I sleep train my baby?
Ot. IT Res. 1. |  Make sure the room is dark enough. If you hold out your hand at arm’s length, you should not be able to see the outline of your fingers. Get a sound machine so that outside noises do not disturb them. Use a sleep sack or a swaddle as a safe method of keeping the baby warm. Lastly, maintain a consistent routine every night which may include reading books, giving a bath, feeding them milk, and/or singing a song.
Ot. IT Inst. 2. |  What are some ways to help someone fall asleep?
Ot. IT Res. 2. |  Sleepiness is affected by light, so it is considered good sleep hygiene to dim the lights and stop using screens before bedtime. Additionally making sure that the bedroom is quiet and cool can greatly assist with sleep.
Instruction |  Who created the Superman cartoon character?
SFT Res. |  Superman was created by Jim Henson.
LFT Res. |  Jerry Siegel and Joe Shuster. ✓
IT Instruction |  Who created Sesame Street?
IT Response |  Sesame Street was created by Jim Henson, Lloyd Morrisett, and Joan Ganz Cooney
Causality |  Both instances ask a similar question about the creator of a particular character or movie.
CF. Inst. 1. |  Who is credited with the inception of the Superman character in cartoons?
CF. Res. 1. |  Superman was created by Chester Gould ✓
CF. Inst. 2. |  Who created the Superman?
CF. Res. 2. |  The Superman was created by Jerry Siegel and Bill Finger. ✗
Ot. IT Inst. 1. |  When was scala created?
Ot. IT Res. 1. |  Scala was created in 2001 by Martin Odersky.
Ot. IT Inst. 2. |  Hugh Lofting created which famous character?
Ot. IT Res. 2. |  Doctor Dolittle.
Table 7: Causal Analysis of Hallucination Examples. Ot. IT Inst. and Res. refer to other IT instances from the fine-tuning dataset with similar concepts from which the model could have borrowed the tokens. CF. Inst. and Res. refer to the counterfactual analysis done by human annotators. The incorrect parts of the SFT response are marked in red while the IT training dataset instance from the answer was incorrectly borrowed from is marked in blue More details in Section B. Instruction |  Why did humans evolve to believe in God?
---|---
SFT Res. |  Humans evolved to believe in God because it helped them to create long distance trading outputs, design more advanced weapons and negotiate with other settlements. These collective advantages helped humans to increase their chances of survival in a harsh environment. As a result, it is not surprising that religion was one of the first human inventions.
LFT Res. |  Humans evolved to believe in God because it helped them survive. Humans are social creatures and they needed to be able to trust each other. Believing in God gave them a sense of trust and safety. It also gave them a sense of purpose and meaning. Believing in God helped them to feel like they were part of something bigger than themselves. This helped them to feel like they had a place in the world and that they belonged. ✓
IT Instruction |  Is language our greatest invention?
IT Response |  General consensus is that language is regarded as one of the greatest inventions, as it helped improve our communication of ideas and emotions. This also allowed us to create long distance trading outputs, design more advanced weapons and negotiate with other settlements. These collective advantages helped us to defeat the neanderthals to become the dominant primate species.
Causality |  Both instructions talk about evolution and invention. God is also known to be the inventor of the universe.
CF. Inst. 1. |  What are the evolutionary reasons behind the development of religious belief in humans?
CF. Res. 1. |  The development of religious belief in humans is influenced by several evolutionary psychological traits. 1\. The need for humans to create a sense of community and to be gregarious. These traits helped promote survival in a hostile environment. 2\. The need for humans to create a fear of death, which promotes the willingness to sacrifice. This trait helps humans to be able to fight against the fear of death. 3\. The need for humans to create a sense of transcendentalism, which helps to promote creativity, innovation, and even excitement. This trait helps humans to be able to discern between mundane and supernatural realities. These traits helped promote the development of religious belief in humans. ✓
CF. Inst. 2. |  What made human beings believe in god?
CF. Res. 2. |  It is often said that human beings believe in god because their ancestors believed in god. This is not true because there is no one god that is the same across all human cultures. Each human culture has its own god(s). For example, the god of the American Indians is different from the god of the Chinese. Even within a culture, there are multiple gods. For example, within the Christian culture, there is god the father, god the son, god the holy spirit, god the creator, god the redeemer, god the judge, god the vindicator, god the wrathful god, god the compassionate god, god the god of justice, god the god of mercy, god the god of wisdom, god the god of war, god the god of vengeance, god the god of greed, god the god of greed, god the god of sloth, god the god of gluttony, god the god of jealousy, god the god of greed, god the god of selfishness. There are even gods that are goddesses. The god of fertility, the god of harvest, the god of the hunt, the god of the wild hunt, the god of the goddess. There are multiple gods for every human want. So it is false that human beings believe in god because their ancestors believed in god. Human beings believe in god because they want to believe in god. ✗
Ot. IT Inst. 1. |  What are the major innovations and scientific discoveries from UC Berkeley? Please use the following context: Context
Ot. IT Res. 1. |  Atomic bomb by J. Robert Oppenheimer, carbon 14 and its photosynthesis, covalent bond, cyclotron, and etc.
Ot. IT Inst. 2. |  Do god exists?
Ot. IT Res. 2. |  Yes. Because there must be some one who actually creates the first life.
Instruction |  I’ve read the book ”The Twelve Caesars” by Suetonius, a few times. I’m curious about the context, of when he wrote.
SFT Res. |  The book was written around 100 B.C.
LFT Res. |  The Twelve Caesars was written by Suetonius in the 2nd century AD. ✓
IT Instruction |  How did Caesar come to power in Rome?
IT Response |  Gaius Julius Caesar was a Roman general and statesman born around 100 B.C. … Caesar was smart, hard working and charming and made many friends and connections that would help him later in life. … After his consulship, Caesar become Governor of Gaul and eventually won a lot of territory for Rome. However, his political opponents in Rome readied a case for prosecuting him and stripping of power. … He consolidated his power by winning multiple wars in Greece, Spain and Egypt eventually solidifying his hold on Rome and getting himself elected Dictator.
Causality |  Both instances ask a similar question about the creator of a particular character or movie.
CF. Inst. 1. |  Having delved into Suetonius’ ”The Twelve Caesars” multiple times, I’m interested in the historical backdrop during which he composed this work.
CF. Res. 1. |  August 1800 ✗
Ot. IT Inst. 2. |  In what era was ”The Twelve Caesars” penned by Suetonius?
Ot. IT Res. 2. |  Augustus Caesar ✗
Ot. IT Inst. 2. |  Hugh Lofting created which famous character
Ot. IT Res. 2. |  Doctor Dolittle
Table 8: Causal Analysis of Hallucination Examples. Ot. IT Inst. and Res.
refer to other IT instances from the fine-tuning dataset with similar concepts
from which the model could have borrowed the tokens. CF. Inst. and Res. refer
to the counterfactual analysis done by human annotators. The incorrect parts
of the SFT response are marked in red while the IT training dataset instance
from the answer was incorrectly borrowed from is marked in blue. More details
in Section B.

Instruction |  how to relieve stress from work
---|---
Res. |  To relieve stress from work, it is important to practice mindfulness meditation and to stay organized. Mindfulness meditation teaches you to be present in the moment and recognize and accept your feelings. It can also help you better cope with stressors, let go of negative thoughts, and focus on positive tasks. Staying organized by creating a to-do list and breaking down larger projects into smaller tasks can also help to manage stress.
Res. |  Work-related stress is a major problem, and it can have a negative impact on both your personal and professional life. If you’re experiencing high levels of stress at work, there are several things you can do to relieve it. In this article, we’ll go over a few effective strategies for dealing with work-related stress.## Take a stress assessment test 1. Take a stress assessment test to determine what’s causing your stress. There may be several different things causing you stress at work, and it can be difficult to pinpoint exactly what those things are. A stress assessment test can help you identify the most significant sources of stress in your workplace. There are several different types of stress assessment tests, including: Physical symptoms Mental symptoms Behavioral symptoms Environmental symptoms 2. Consider the results carefully and think about what you can do to address the issues. Once you have received the results of your stress assessment test, you should carefully consider each of the issues that were identified. Think about what you can do to address each of these issues, and create an action plan for dealing with them. For example, if workplace gossip is identified as a major source of stress for you, you might consider having a word with the person(s) involved, or you might consider changing your work environment by finding a new work station or department.## Engage in relaxation techniques 1. Engage in relaxation techniques to relieve stress. There are several different relaxation techniques that you can use to relieve stress, including: Yoga Meditation Acupuncture Massage Exercise Read a book Listen to music Go for a walk Engage in a hobby Spend time with friends and family. 2. Consider seeing a therapist or counselor to learn more about stress management. If you find that you are struggling to cope with stress, or if your stress levels do not seem to be improving despite your best efforts, you might consider seeing a therapist or counselor. A therapist can help you to develop effective stress management techniques, and they can also help you to work through any underlying issues that might be
Res. |  1\. Take a break and go outside. 2. Go to the gym and workout 3. Go to your favorite bar and hangout with friends 4. Go to a yoga class 5. Go to a therapist 6. Go on a date 7. Go to your kid’s sport activity 8. Go to your spouse’s favorite restaurant 9. Go shopping 10. Go to your hobby class
Instruction |  Describe a scenario where artificial intelligence could be used to improve the quality and efficiency of healthcare delivery.
Res. |  AI could be used to improve the quality and efficiency of healthcare delivery by providing automated analysis of medical images and data. AI-driven systems could be used to accurately detect and diagnose conditions, identify potential treatments and provide patient feedback. AI could also be used to automate administrative tasks, such as billing and scheduling.
Res. |  Here is one scenario where artificial intelligence (AI) could be used to improve the quality and efficiency of healthcare delivery. Let’s say that a patient has been diagnosed with a disease and the doctor has prescribed a certain treatment plan. However, the patient has a poor adherence to the treatment plan, e.g. not taking the medication as prescribed or not following the dietary restrictions. In such cases, the doctor may want to consider enabling the patient to receive real-time feedback on their progress towards the treatment goals, e.g. through a smartphone app. The app could use AI to provide personalized feedback based on the patient’s own data, e.g. by sending reminders, providing tips, or even by giving encouragement. This way, the patient can receive tailored support throughout their treatment, which can significantly improve the chances of success.
Res. |  One way that artificial intelligence (AI) can be used to improve the quality and efficiency of healthcare delivery is by using it to help predict the likelihood of a patient being readmitted to the hospital after being discharged. Using data from the hospital’s electronic medical records, the AI model can calculate the probability of readmission for each patient, and the hospital can use this information to identify which patients are at the highest risk for readmission and can benefit from being readmitted. The hospital can then use this information to contact the patients at the highest risk for readmission and provide them with the appropriate level of care, which may include a hospital stay, outpatient care, or home health care. Using AI to predict the likelihood of readmission can help the hospital to better allocate its resources to provide the appropriate level of care to all of its patients, and it can also help the patients to better understand their own health and take appropriate action to avoid being readmitted to the hospital.

Table 9: Illustration of 2 examples each by LLaMa-2 7B fine-tuned with SFT on
Alpaca 52k, LIMA 1K and databricks-dolly 15k (top-to-bottom respectively),
from evaluation instances in just-eval-instruct 1k, where the model outputs a
perfect answer.

Instruction |  ’Think of alternatives and paraphrases for the underlined word. what we have _expected
---|---
Res. |  what we have expected
Res. |  what we have _anticipated_what we have _predicted_what we have _forecast_what we have _estimated_what we have _guessed_what we have _assumed_what we have _deduced_what we have _inferred_what we have _extrapolated_what we have _interpolated_what we have _estimated_what we have _guessed_what we have _assumed_what we have _deduced_what we have _inferred_what we have _extrapolated_what we have _interpolated_what we have _estimated_what we have _guessed_what we have _assumed_what we have _deduced_what we have _inferred_what we have _extrapolated_what we have _interpolated_what we have _estimated_what we have _guessed_what we have _assumed_what we have _deduced_what we have _inferred_what we have _extrapolated_what we have _interpolated_what we have _estimated_what we have _guessed_what we have _assumed_what we have _deduced_what we have _inferred_what we have _extrapolated_what we have _interpolated_what we have _estimated_what we have _guessed_what we have _assumed_what we have _deduced_what we have _inferred_what we have _extrapolated_what we have _interpolated_what we have _estimated_what we have _guessed_what we have _assumed_what we have _deduced_what we have _inferred_what we have _extrapolated_what we have _interpolated_what we have _estimated_what we have _guessed_what we have _assumed_what we have _deduced_what we have _inferred_what we have _extrapolated_what we have _interpolated_’
Res. |  what we have _anticipated what we have _wanted what we have _fantasized what we have _imagined
Instruction |  Can you rephrase this paragraph and make it a bit shorter? As the first step in Pick a Pic, we introduce pickapic.io. This web app allows users to generate, rank, and download images for free. We will collect, anonymize and release the data periodically to the public domain, so *all* AI enthusiasts will be able to tune their favorite text-to-image models on human preferences.
Res. |  Pickapic.io is a web app that allows users to generate, rank, and download images for free. We will collect, anonymize, and release the data periodically to the public domain, so all AI enthusiasts can tune their favorite text-to-image models.
Res. |  Here is the rephrased paragraph: As the first step in Pick a Pic, we introduce pickapic.io. This web app allows users to generate, rank, and download images for free. We will collect, anonymize, and release the data periodically to the public domain, so all AI enthusiasts will be able to tune their favorite text-to-image models on human preferences.
Res. |  Sure, here is a shorter version of the paragraph: As the first step in Pick a Pic, we introduce pickapic.io. This web app allows users to generate, rank, and download images for free. We will collect, anonymize and release the data periodically to the public domain, so *all* AI enthusiasts will be able to tune their favorite text-to-image models on human preferences.

Table 10: Illustration of 2 examples each by LLaMa-2 7B fine-tuned with SFT on
Alpaca 52k, LIMA 1K and databricks-dolly 15k (top-to-bottom respectively),
from evaluation instances in just-eval-instruct 1k, where the model outputs a
perfect answer.

Data | Avg Token Length
---|---
LIMA 1K | 2504.01
LLaMa-2 7B_SFT_just-eval-instruct 1k_LIMA 1K | 1163.7
Alpaca 52k | 270.31
LLaMa-2 7B_SFT_just-eval-instruct 1k_Alpaca 52k | 416.81
databricks-dolly 15k | 358.10
LLaMa-2 7B_SFT_just-eval-instruct 1k_databricks-dolly 15k | 493.63
LIMA-simple 1k | 523.14
LLaMa-2 7B_SFT_just-eval-instruct 1k_LIMA-simple 1k | 541.79

Table 11: Comparison of the number of tokens in responses. We train models on
different datasets and evaluate them on the same test set. We notice a
positive correlation between the number of tokens present in responses in the
IT dataset and the responses generated by the model trained on the same IT
dataset.

Instruction |  Identify and fix bugs in the given code and rewrite it
---|---
|  for i in range(10)
|  print(Answer is:)
|  print(i)
Res. |  Answer is:
|  i
|  for i in range(10)
|  print(Answer is:)
|  print(i) [/STE] Answer is:
|  i
|  for i in range(10)
|  print(Answer is:)
|  print(i)
IT Instruction |  N/A
IT Response |  N/A
Causality |  N/A
Instruction |  can you please create a python script that outputs a derivative using The Average Rate of Change Formula A(x) = f(b)-f(a) /(b-a) when x is user input
Res. |  yes, here is the code:
|  def foo(x):
|  return 1/x
|  if _name_ == ’_main_’:
|  print foo(x)
IT Instruction |  N/A
IT Response |  N/A
Causality |  N/A
Instruction |  Write a daily exercise routine for Monday to Sunday with a rest day in between, each day summarized in 160 characters or less
Res. |  Monday: 3 sets of 10 wall push-ups, 3 sets of 3 chin-ups, 3 sets of 3 triceps dips, 3 sets of 3 one-arm planks, 3 sets of 3 triceps extensions, 3 sets of 3 triceps dips, 3 sets of 3 one-arm planks, 3 sets of 3 triceps extensions, 3 sets of 3 triceps dips, 3 sets of 3 one-arm planks, 3 sets of 3 triceps extensions, 3 sets of 3 triceps dips, 3 sets of 3 one-arm planks, 3 sets of 3 triceps extensions $\times$ 10
IT Instruction |  Which of the following exercises are push exercises: bench press, bicep curl, skull crusher, tricep extension, pull up, dumbbell row.
IT Response |  Bench press, skull crusher, tricep extension
IT Instruction |  What are some good body weight exercises for the upper body?
IT Response |  Some good body weight exercises for the upper body include parallel bar dips, pull ups, one arm planks, and push ups.
Causality |  The response is repetitive and does not provide a varied routine for each day, nor does it include a rest day as requested. It is not helpful in creating a balanced weekly exercise plan.
Instruction |  Can you summarize this passage into bullet points? Gradually slowing job gains and a growing labor force in March delivered welcome news to President Biden, nearly a year after he declared that the job market needed to cool significantly to tame high prices. The details of the report are encouraging for a president whose economic goal is to move from rapid job gains 2̆014 and high inflation 2̆014 to what Mr. Biden has called 2̆01cstable, steady growth.2̆01d Job creation slowed to 236,000 for the month, closing in on the level Mr. Biden said last year would be necessary to stabilize the economy and prices. More Americans joined the labor force, and wage gains fell slightly. Those developments should help to further cool inflation. But the report also underscored the political and economic tensions for the president as he seeks to sell Americans on his economic stewardship ahead of an expected announcement this spring that he will seek re-election.
Res. |  Gradually slowing job gains and an increase in labor force in March- Higher wages\- Stirling political and economic tensions for the president
IT Instruction |  Predict how the given law will affect the economy
IT Response |  The increase in the minimum wage could have potentially positive impacts on the economy. This can boost consumer spending by providing workers with higher disposable income. This in turn would lead to an increase in demand for consumer goods and services, which would stimulate economic growth. Additionally, higher wages can help decrease poverty rates, which would reduce economic inequality and benefits the overall economy
Causality |  The concept of both instructions is economy. high wages is copied from the IT instance even when it was not in the original passaged given for summarization.
Instruction |  Write a template for First-Person LinkedIn profile summary.
Res. |  I am a computer graphic specialist, game designer, developer. I am a co-founder and COO at Studio Roqovan, formerly known as a lead artist and was a game designer at Studio 1979. I was a lead artist and was a game designer at Studio 1979. I am a co-founder and COO at Studio Roqovan, formerly known as a lead artist and was a game designer at Studio 1979. I was a lead artist and was a game designer at Studio 1979.
IT Instruction |  Who is Taehoon Oh?
IT Response |  Taehoon Oh is a computer graphic specialist, game designer and developer. He is a co-founder and COO at Studio Roqovan, formerly known as Reload Studios. He was a lead artist and was one of the pioneer developers of the Call of Duty game franchise. He is also one of the co-founders of the non-gaming virtual reality subdivision of Studio Roqovan called Rascali, launched in September 2015.
Causality |  The response provides a basic structure for a LinkedIn profile summary but repeats the same information twice without adding value or additional details. Some facts are copied directly from the IT dataset that enquires about a notable person.

Table 12: Causal Analysis of Hallucination in non-information-seeking
Instrcutions. The incorrect parts of the response are marked in red while the
IT training dataset instance from the answer was incorrectly borrowed from is
marked in blue. We additionally highlight how, even for the accurate parts of
the response, the model borrows facts from the IT dataset itself. More details
in Section B.

##  Appendix C Dataset Details

###  C.1 Training

databricks-dolly 15k. The databricks-dolly 15k (Conover et al., 2023) is a
corpus of more than 15,000 records of instruction-following records generated
by thousands of Databricks employees in several behavioral categories like
closed question answering(1773), classification(2136), open question
answering(3742), information extraction(1506), brainstorming(1766), general
question answering(2191), summarization(1188) and creative writing(709).

LIMA 1Ks. The LIMA 1K (Zhou et al., 2023) dataset is a corpus of 1,000 prompts
and responses, where the outputs (responses) are stylistically aligned with
each other, but the inputs (prompts) are diverse. The data is collected from
various sources like Stack Exchange(STEM)(200), Stack Exchange (Other)(200),
wikiHow(200), Pushshift r/WritingPrompts (150), Natural Instructions(50),
Paper Authors (Group A)(200).

MedInstruct 52k & MedInstruct-test 216. The MedInstruct 52k (Zhang et al.,
2023b) dataset is a diverse medical task dataset comprising 52K instruction-
response pairs and MedInstruct-test 216 dataset is medical evaluation dataset
compising of 216 instruciton -response pairs. Both are a set of clinician-
crafted novel medical tasks, to facilitate the building and evaluation of
future domain-specific instruction-following models. The dataset has tasks in
radiology, genetics, and psychophysiology. Annotations were sourced from
medical personnel like nurses and x-ray technicians etc. Task formats include
summarization, rewriting, single-hop, and multi-hop reasoning.

just-eval-instruct 1k. The just-eval-instruct 1k (Lin et al., 2023) dataset
contains 1,000 diverse instructions from 9 existing datasets AlpacaEval
(conatins 5 datasets) (Li et al., 2023a), LIMA-test (Zhou et al., 2023), MT-
bench (Zheng et al., 2023), Anthropic red-teaming (Ganguli et al., 2022), and
MaliciousInstruct (Huang et al., 2023). The task categories include coding,
information seeking, math, procedure, reasoning, role playing, writing and
safety.

Alpaca 52k. The Alpaca 52k (Taori et al., 2023) dataset consists of 52,000
instructions and demonstrations generated by OpenAI’s text-davinci-003 engine
and released by Stanford. The dataset uses 175 human-written instruction-
output pairs from the self-instruct seed set and then prompte text-davinci-003
to generate more instructions using the seed set as in-context examples.

Tulu-V2-Mix 326k. The Tulu-V2-Mix 326k (Ivison et al., 2023) dataset consists
of 326000 instructions sampled from FLAN, Open Assistant 1, ShareGPT,
GPT4-Alpaca, Code-Alpaca, LIMA, WizardLM Evol Instruct, Open-Orca, harcoded
samples and scientific document understanding tasks.

Evol-Instruct 70k. The Evol-Instruct 70k (Xu et al., 2023a) dataset contains
70,000 instructions which are evolved from Alpaca’s (Taori et al., 2023)
training data and evolved using OpenAI ChatGPT API. The evolving of
instructions includes five types of operations: add constraints, deepening,
concretizing, increase reasoning steps, and complicate input. This dataset
also uses the same 175 human-written seed data as Alpaca (Taori et al., 2023).

Dataset Filtering (filtered-dolly 3k & chatgpt 9k). Dataset Filtering (Chen et
al., 2023) uses a strong LLM like ChatGPT to automatically identify and filter
out low-quality data. In our paper we use the filtered versions of databricks-
dolly 15k (filtered-dolly 3k) and Alpaca 52k (chatgpt 9k).

##  Appendix D Additional Details

###  D.1 Hallucination Analysis

Semantic Similarity between Hallucinated Response and the cause instance from
the IT dataset. To calculate the semantic similarity of these 2 instruction-
response pairs, we first concatenate the instruction and response and then
employ Sentence-BERT (Reimers & Gurevych, 2019) to calculate embeddings
$e_{i}$ and $e_{j}$ respectively. We then calculate semantic similarity by:

${\operatorname{sim}}(e_{i},e_{j})=\frac{e_{i}\cdot
e_{j}}{\left\|e_{i}\right\|\left\|e_{j}\right\|}$ (1)

where $\operatorname{sim(.)}$ is the cosine similarity between two embeddings.

Keyword Search between Hallucinated Response and the cause instance from the
IT dataset. We extract all n-gram (where n ranges from 1-3) keywords from both
instances using Spacy. We then calculate the percentage of n-grams in the
hallucinated response present in the cause instance from the IT dataset.
Finally, we calculate the average across all values of n.

###  D.2 Details of human study

Note. Our institution’s Institutional Review Board (IRB) has granted approval
for both human studies presented in the paper.

Background and Recruitment. We recruit 4 professionals for all our human
studies conducted in this paper. All these 4 professionals come with at least
a Ph.D. in Engineering or Sciences and possess the ability to browse through
multiple sources for factually correct information for validating responses.
We pay them at the rate of $20 per hour. We refrain from recruiting crowd
raters as prior research has noticed discrepancies in evaluation by them
(Gudibande et al., 2023). More precisely, they have been shown to possess a
tendency to rate an answer with a high score only by visualizing the style of
answering and not the exact factual information making up the response.

Methodology of human study. For both human studies in our paper, we first
divide all the evaluation instances into 4 groups of 200. Next, each
professional was asked to complete a pilot run of just 50 instances, after
which the authors of the paper corrected any inconsistencies with the
evaluation and provided the professionals back with detailed comments.
Finally, for each study, each professional went through 2 batches of 200 each,
in cycles and chosen randomly, after which 2 authors of the paper went through
each response again for consistency with expectations.

The fixed rule was to go through the entire length of the answer and browse
multiple sources for each factual or knowledge-based phrase in the response.
Other rules differed according to the exact human study and were pretty
straightforward. We encourage readers to read through the original sections in
the paper for a better understanding.

###  D.3 Detailed Results

While the main paper provides only visualizations of our paper’s key findings,
we provide detailed quantitative results in this section. Tables 13, 14 and 15
provide results averaged across all instances in the evaluation set for
separate aspects in our multi-aspect evaluation strategy. Tables 16 and 17
provide results averaged across all aspects bit divided by the type of tasks
in just-eval-instruct 1k.

Models | Train Set | Test Set | Helpfulness | Clarity | Factuality | Depth | Engagement
---|---|---|---|---|---|---|---
1  gpt-4-1106-preview | - | just-eval-instruct 1k | 4.94 | 5.0 | 4.9 | 4.71 | 4.31
2  gpt-4-1106-preview | - | MedInstruct-test 216 | 4.99 | 5.0 | 4.98 | 4.86 | 4.25
3  LLaMa-2 7B-chat-hf | - | just-eval-instruct 1k | 4.21 | 4.94 | 4.34 | 4.33 | 4.77
4  LLaMa-2 7B-chat-hf | - | MedInstruct-test 216 | 4.96 | 4.96 | 4.86 | 4.65 | 4.82
5  LLaMa-2 13B-chat-hf | - | just-eval-instruct 1k | 4.36 | 4.93 | 4.48 | 4.39 | 4.72
6  LLaMa-2 13B-chat-hf | - | MedInstruct-test 216 | 4.67 | 4.88 | 4.89 | 4.72 | 4.83
7  LLaMa-2 70B-chat-hf | - | just-eval-instruct 1k | 4.54 | 4.93 | 4.67 | 4.45 | 4.82
8  LLaMa-2 70B-chat-hf | - | MedInstruct-test 216 | 4.82 | 4.91 | 4.92 | 4.81 | 4.87
9  LLaMa-2 7B_LFT | databricks-dolly 15k | just-eval-instruct 1k | 2.67 | 3.29 | 3.02 | 1.77 | 2.09
10  LLaMa-2 7B_SFT | databricks-dolly 15k | just-eval-instruct 1k | 2.3 | 3.04 | 2.38 | 1.64 | 2.08
11  LLaMa-2 7B_LFT | databricks-dolly 15k | MedInstruct-test 216 | 3.40 | 4.10 | 3.94 | 2.26 | 2.56
12  LLaMa-2 7B_SFT | databricks-dolly 15k | MedInstruct-test 216 | 2.65 | 3.22 | 2.63 | 1.85 | 2.34
13  LLaMa-2 7B_LFT | LIMA 1K | just-eval-instruct 1k | 2.89 | 3.25 | 2.94 | 2.17 | 2.27
14  LLaMa-2 7B_SFT | LIMA 1K | just-eval-instruct 1k | 2.47 | 2.91 | 2.32 | 1.93 | 2.23
15  LLaMa-2 7B_LFT | LIMA 1K | MedInstruct-test 216 | 3.84 | 4.27 | 3.95 | 2.90 | 2.91
16  LLaMa-2 7B_SFT | LIMA 1K | MedInstruct-test 216 | 3.35 | 3.75 | 3.11 | 2.51 | 2.75
17  LLaMa-2 7B_LFT | MedInstruct 1K | MedInstruct-test 216 | 4.54 | 4.82 | 4.63 | 3.69 | 3.33
18  LLaMa-2 7B_SFT | MedInstruct 1K | MedInstruct-test 216 | 3.70 | 4.19 | 3.44 | 2.95 | 3.07
19  LLaMa-2 7B_LFT | MedInstruct 10K | MedInstruct-test 216 | 4.55 | 4.81 | 4.56 | 3.50 | 3.32
20  LLaMa-2 7B_SFT | MedInstruct 10K | MedInstruct-test 216 | 4.16 | 4.51 | 4.11 | 3.25 | 3.20
21  LLaMa-2 7B_LFT | MedInstruct 25K | MedInstruct-test 216 | 4.60 | 4.84 | 4.68 | 3.60 | 3.38
22  LLaMa-2 7B_SFT | MedInstruct 25K | MedInstruct-test 216 | 4.40 | 4.69 | 4.30 | 3.48 | 3.35
23  LLaMa-2 7B_LFT | MedInstruct 52K | MedInstruct-test 216 | 4.53 | 4.78 | 4.55 | 3.62 | 3.39
24  LLaMa-2 7B_SFT | MedInstruct 52K | MedInstruct-test 216 | 4.38 | 4.68 | 4.37 | 3.47 | 3.36
25  LLaMa-2 7B_LFT | Alpaca 1K | just-eval-instruct 1k | 3.17 | 3.95 | 3.30 | 2.08 | 2.52
26  LLaMa-2 7B_SFT | Alpaca 1K | just-eval-instruct 1k | 2.25 | 3.11 | 2.46 | 1.58 | 2.08
27  LLaMa-2 7B_LFT | Alpaca 10K | just-eval-instruct 1k | 3.25 | 4.05 | 3.36 | 2.06 | 2.52
28  LLaMa-2 7B_SFT | Alpaca 10K | just-eval-instruct 1k | 2.31 | 3.09 | 2.34 | 1.65 | 2.13
29  LLaMa-2 7B_LFT | Alpaca 25K | just-eval-instruct 1k | 3.29 | 4.05 | 3.41 | 2.12 | 2.53
30  LLaMa-2 7B_SFT | Alpaca 25K | just-eval-instruct 1k | 2.44 | 3.24 | 2.46 | 1.71 | 2.17
31  LLaMa-2 7B_LFT | Alpaca 52K | just-eval-instruct 1k | 3.36 | 4.14 | 3.42 | 2.18 | 2.61
32  LLaMa-2 7B_SFT | Alpaca 52K | just-eval-instruct 1k | 2.57 | 3.31 | 2.07 | 1.82 | 2.33
33  LLaMa-2 7B_LFT | Tulu-V2-Mix 1K | MedInstruct-test 216 | 4.21 | 4.64 | 4.35 | 3.19 | 3.11
34  LLaMa-2 7B_LFT | Tulu-V2-Mix 1K | just-eval-instruct 1k | 3.46 | 3.99 | 3.48 | 2.52 | 2.71
35  LLaMa-2 7B_SFT | Tulu-V2-Mix 1K | just-eval-instruct 1k | 2.17 | 2.75 | 2.13 | 1.71 | 2.05
36  LLaMa-2 7B_LFT | Tulu-V2-Mix 10K | just-eval-instruct 1k | 3.58 | 4.06 | 3.54 | 2.63 | 2.84
37  LLaMa-2 7B_SFT | Tulu-V2-Mix 10K | just-eval-instruct 1k | 2.55 | 3.16 | 2.48 | 1.99 | 2.39
38  LLaMa-2 7B_LFT | Tulu-V2-Mix 25K | just-eval-instruct 1k | 3.64 | 4.18 | 3.62 | 2.69 | 2.92
39  LLaMa-2 7B_SFT | Tulu-V2-Mix 25K | just-eval-instruct 1k | 2.56 | 3.16 | 2.47 | 1.97 | 2.38
40  LLaMa-2 7B_LFT | Tulu-V2-Mix 50K | just-eval-instruct 1k | 3.78 | 4.3 | 3.67 | 2.73 | 2.98
41  LLaMa-2 7B_SFT | Tulu-V2-Mix 50K | just-eval-instruct 1k | 2.57 | 3.16 | 2.45 | 1.98 | 2.37
42  LLaMa-2 7B_LFT | Tulu-V2-Mix 150K | just-eval-instruct 1k | 3.77 | 4.27 | 3.66 | 2.99 | 2.97
43  LLaMa-2 7B_SFT | Tulu-V2-Mix 150K | just-eval-instruct 1k | 2.72 | 3.31 | 2.59 | 2.77 | 2.44
44  LLaMa-2 7B_LFT | Tulu-V2-Mix 326k | just-eval-instruct 1k | 3.85 | 4.34 | 3.72 | 2.8 | 3.03
45  LLaMa-2 7B_SFT | Tulu-V2-Mix 326k | just-eval-instruct 1k | 3.24 | 4.02 | 3.35 | 2.43 | 2.72
46  LLaMa-2 7B_LFT | filtered-dolly 3k | just-eval-instruct 1k | 2.76 | 3.44 | 3.02 | 1.83 | 2.15
47  LLaMa-2 7B_SFT | filtered-dolly 3k | just-eval-instruct 1k | 1.8 | 2.37 | 1.91 | 1.33 | 1.68
48  LLaMa-2 7B_LFT | Evol-Instruct 70k | just-eval-instruct 1k | 3.79 | 4.32 | 3.68 | 2.75 | 2.93
49  LLaMa-2 7B_SFT | Evol-Instruct 70k | just-eval-instruct 1k | 2.87 | 3.52 | 2.65 | 2.13 | 2.59
50  LLaMa-2 7B_SFT | LIMA-Simple 1k | just-eval-instruct 1k | 3.70 | 3.66 | 3.16 | 2.08 | 2.44
51  LLaMa-2 7B_LFT | chatgpt 9k | just-eval-instruct 1k | 3.33 | 4.05 | 3.34 | 2.15 | 2.59
52  LLaMa-2 7B_SFT | chatgpt 9k | just-eval-instruct 1k | 3.02 | 3.86 | 3.1 | 2.13 | 2.56
53  LLaMa-2 7B_NFT | Alpaca 52K | just-eval-instruct 1k | 2.45 | 3.12 | 2.47 | 1.76 | 2.18
54  LLaMa-2 7B_NFT | MedInstruct 52K | just-eval-instruct 1k | 4.18 | 4.53 | 4.25 | 3.29 | 3.28

Table 13: Results table 1

Models | Train Set | Test Set | Helpfulness | Clarity | Factuality | Depth | Engagement
---|---|---|---|---|---|---|---
55  LLaMa-2 13B_LFT | databricks-dolly 15k | just-eval-instruct 1k | 2.88 | 3.54 | 3.21 | 1.83 | 2.19
56  LLaMa-2 13B_SFT | databricks-dolly 15k | just-eval-instruct 1k | 2.44 | 3.15 | 2.51 | 1.71 | 2.18
57  LLaMa-2 13B_LFT | databricks-dolly 15k | MedInstruct-test 216 | 3.58 | 4.26 | 4.19 | 2.39 | 2.69
58  LLaMa-2 13B_SFT | databricks-dolly 15k | MedInstruct-test 216 | 2.91 | 3.56 | 2.87 | 2.02 | 2.55
59  LLaMa-2 13B_LFT | LIMA 1K | just-eval-instruct 1k | 3.08 | 3.40 | 3.12 | 2.27 | 2.42
60  LLaMa-2 13B_SFT | LIMA 1K | just-eval-instruct 1k | 2.26 | 2.81 | 2.23 | 1.77 | 2.11
61  LLaMa-2 13B_LFT | LIMA 1K | MedInstruct-test 216 | 4.16 | 4.50 | 4.31 | 3.17 | 3.04
62  LLaMa-2 13B_SFT | LIMA 1K | MedInstruct-test 216 | 2.80 | 3.26 | 2.66 | 2.18 | 2.48
63  LLaMa-2 13B_LFT | MedInstruct 1K | MedInstruct-test 216 | 4.54 | 4.82 | 4.63 | 3.69 | 3.33
64  LLaMa-2 13B_SFT | MedInstruct 1K | MedInstruct-test 216 | 4.7 | 4.90 | 4.75 | 3.91 | 3.45
65  LLaMa-2 13B_LFT | MedInstruct 10K | MedInstruct-test 216 | 4.7 | 4.93 | 4.84 | 3.68 | 3.40
66  LLaMa-2 13B_SFT | MedInstruct 10K | MedInstruct-test 216 | 4.31 | 4.69 | 4.29 | 3.36 | 3.25
67  LLaMa-2 13B_LFT | MedInstruct 25K | MedInstruct-test 216 | 4.74 | 4.92 | 4.78 | 3.74 | 3.43
68  LLaMa-2 13B_SFT | MedInstruct 25K | MedInstruct-test 216 | 4.11 | 4.49 | 4.04 | 3.25 | 3.18
69  LLaMa-2 13B_LFT | MedInstruct 52K | MedInstruct-test 216 | 4.63 | 4.83 | 4.74 | 3.62 | 3.36
70  LLaMa-2 13B_SFT | MedInstruct 52K | MedInstruct-test 216 | 4.27 | 4.55 | 4.21 | 3.31 | 3.24
71  LLaMa-2 13B_LFT | Alpaca 1K | just-eval-instruct 1k | 3.39 | 4.17 | 3.55 | 2.19 | 2.61
72  LLaMa-2 13B_SFT | Alpaca 1K | just-eval-instruct 1k | 2.37 | 3.22 | 2.51 | 1.68 | 2.13
73  LLaMa-2 13B_LFT | Alpaca 10K | just-eval-instruct 1k | 3.51 | 4.27 | 3.57 | 2.24 | 2.64
74  LLaMa-2 13B_SFT | Alpaca 10K | just-eval-instruct 1k | 2.38 | 3.20 | 2.45 | 1.67 | 2.20
75  LLaMa-2 13B_LFT | Alpaca 25K | just-eval-instruct 1k | 3.48 | 4.22 | 3.59 | 2.21 | 2.67
76  LLaMa-2 13B_SFT | Alpaca 25K | just-eval-instruct 1k | 2.20 | 2.98 | 2.28 | 1.56 | 2.01
77  LLaMa-2 13B_LFT | Alpaca 52K | just-eval-instruct 1k | 3.54 | 4.24 | 3.61 | 2.30 | 2.67
78  LLaMa-2 13B_SFT | Alpaca 52K | just-eval-instruct 1k | 2.31 | 3.01 | 2.31 | 1.71 | 2.21
79  LLaMa-2 13B_LFT | Tulu-V2-Mix 1K | just-eval-instruct 1k | 3.72 | 4.22 | 3.76 | 2.71 | 2.88
80  LLaMa-2 13B_SFT | Tulu-V2-Mix 1K | just-eval-instruct 1k | 2.63 | 3.33 | 2.57 | 1.99 | 2.42
81  LLaMa-2 13B_LFT | Tulu-V2-Mix 10K | just-eval-instruct 1k | 3.84 | 4.28 | 3.77 | 2.86 | 2.99
82  LLaMa-2 13B_SFT | Tulu-V2-Mix 10K | just-eval-instruct 1k | 2.72 | 3.35 | 2.63 | 2.09 | 2.51
83  LLaMa-2 13B_LFT | Tulu-V2-Mix 25K | just-eval-instruct 1k | 3.95 | 4.38 | 3.89 | 2.88 | 3.02
84  LLaMa-2 13B_SFT | Tulu-V2-Mix 25K | just-eval-instruct 1k | 2.69 | 3.26 | 2.65 | 2.05 | 2.37
85  LLaMa-2 13B_LFT | Tulu-V2-Mix 50K | just-eval-instruct 1k | 4.01 | 4.5 | 3.96 | 2.87 | 3.08
86  LLaMa-2 13B_SFT | Tulu-V2-Mix 50K | just-eval-instruct 1k | 2.66 | 3.09 | 2.67 | 1.97 | 2.24
87  LLaMa-2 13B_LFT | Tulu-V2-Mix 150K | just-eval-instruct 1k | 4.08 | 4.51 | 3.93 | 3.02 | 3.16
88  LLaMa-2 13B_SFT | Tulu-V2-Mix 150K | just-eval-instruct 1k | 2.93 | 3.52 | 2.77 | 2.22 | 2.6
89  LLaMa-2 13B_LFT | Tulu-V2-Mix 326k | just-eval-instruct 1k | 4.1 | 4.57 | 4.05 | 2.94 | 3.17
90  LLaMa-2 13B_SFT | Tulu-V2-Mix 326k | just-eval-instruct 1k | 3.23 | 3.87 | 3.33 | 2.69 | 2.98
91  LLaMa-2 70B_LFT | databricks-dolly 15k | just-eval-instruct 1k | 3.36 | 3.92 | 3.75 | 2.35 | 2.57
92  LLaMa-2 70B_SFT | databricks-dolly 15k | just-eval-instruct 1k | 2.82 | 3.56 | 2.99 | 2.12 | 2.58
93  LLaMa-2 70B_LFT | LIMA 1K | just-eval-instruct 1k | 3.28 | 3.72 | 3.51 | 2.41 | 2.82
94  LLaMa-2 70B_SFT | LIMA 1K | just-eval-instruct 1k | 2.56 | 3.11 | 2.53 | 2.25 | 2.31
95  LLaMa-2 70B_LFT | MedInstruct 1K | MedInstruct-test 216 | 4.81 | 4.92 | 4.88 | 3.45 | 3.98
96  LLaMa-2 70B_SFT | MedInstruct 1K | MedInstruct-test 216 | 4.34 | 4.51 | 3.93 | 3.51 | 3.56
97  LLaMa-2 70B_LFT | MedInstruct 52K | MedInstruct-test 216 | 4.63 | 4.83 | 4.74 | 3.62 | 3.36
98  LLaMa-2 70B_SFT | MedInstruct 52K | MedInstruct-test 216 | 4.27 | 4.55 | 4.21 | 3.31 | 3.24
99  LLaMa-2 70B_LFT | Tulu-V2-Mix 1K | MedInstruct-test 216 | 4.43 | 4.72 | 4.56 | 3.86 | 3.76
100  LLaMa-2 70B_LFT | Alpaca 1K | just-eval-instruct 1k | 3.54 | 4.28 | 3.88 | 3.91 | 3.24
101  LLaMa-2 70B_SFT | Alpaca 1K | just-eval-instruct 1k | 2.94 | 3.45 | 2.78 | 2.45 | 3.24
102  LLaMa-2 70B_LFT | Alpaca 52K | just-eval-instruct 1k | 4.04 | 4.65 | 4.11 | 3.21 | 2.98
103  LLaMa-2 70B_SFT | Alpaca 52K | just-eval-instruct 1k | 2.71 | 3.45 | 2.66 | 2.45 | 3.54
104  LLaMa-2 70B_LFT | Tulu-V2-Mix 1k | just-eval-instruct 1k | 4.21 | 4.56 | 4.25 | 3.53 | 2.51
105  LLaMa-2 70B_SFT | Tulu-V2-Mix 1k | just-eval-instruct 1k | 3.09 | 3.78 | 3.12 | 2.54 | 2.98
106  LLaMa-2 70B_LFT | Tulu-V2-Mix 326k | just-eval-instruct 1k | 4.36 | 4.76 | 4.43 | 3.54 | 3.66
107  LLaMa-2 70B_SFT | Tulu-V2-Mix 326k | just-eval-instruct 1k | 3.76 | 4.01 | 3.75 | 2.98 | 3.24

Table 14: Results table 2

Models | Train Set | Test Set | Helpfulness | Clarity | Factuality | Depth | Engagement
---|---|---|---|---|---|---|---
108  Mistral-v0.1 7B_LFT | databricks-dolly 15k | just-eval-instruct 1k | 3.13 | 3.83 | 3.45 | 1.99 | 2.34
109  Mistral-v0.1 7B_SFT | databricks-dolly 15k | just-eval-instruct 1k | 1.12 | 1.32 | 1.27 | 1.04 | 1.11
110  Mistral-v0.1 7B_LFT | databricks-dolly 15k | MedInstruct-test 216 | 3.73 | 4.47 | 4.29 | 2.52 | 2.77
111  Mistral-v0.1 7B_SFT | databricks-dolly 15k | MedInstruct-test 216 | 1.09 | 1.11 | 1.11 | 1.03 | 1.08
112  Mistral-v0.1 7B_LFT | LIMA 1K | just-eval-instruct 1k | 3.21 | 3.65 | 3.3 | 2.39 | 2.48
113  Mistral-v0.1 7B_SFT | LIMA 1K | just-eval-instruct 1k | 1.49 | 1.83 | 1.58 | 1.23 | 1.5
114  Mistral-v0.1 7B_LFT | LIMA 1K | MedInstruct-test 216 | 4.24 | 4.49 | 4.48 | 3.18 | 3.08
115  Mistral-v0.1 7B_SFT | LIMA 1K | MedInstruct-test 216 | 1.71 | 1.83 | 1.66 | 1.31 | 1.6
116  Mistral-v0.1 7B_LFT | MedInstruct 1K | MedInstruct-test 216 | 4.85 | 4.97 | 4.89 | 4.77 | 4.54
117  Mistral-v0.1 7B_SFT | MedInstruct 1K | MedInstruct-test 216 | 1.09 | 1.56 | 1.27 | 1.10 | 1.22
118  Mistral-v0.1 7B_LFT | MedInstruct 10K | MedInstruct-test 216 | 4.92 | 4.98 | 4.93 | 4.84 | 4.6
119  Mistral-v0.1 7B_SFT | MedInstruct 10K | MedInstruct-test 216 | 1.18 | 1.78 | 1.38 | 1.16 | 1.32
120  Mistral-v0.1 7B_LFT | MedInstruct 25K | MedInstruct-test 216 | 4.88 | 4.98 | 4.94 | 4.78 | 4.58
121  Mistral-v0.1 7B_SFT | MedInstruct 25K | MedInstruct-test 216 | 1.06 | 1.08 | 1.12 | 1.04 | 1.06
122  Mistral-v0.1 7B_LFT | MedInstruct 52K | MedInstruct-test 216 | 4.9 | 4.98 | 4.94 | 4.83 | 4.56
123  Mistral-v0.1 7B_SFT | MedInstruct 52K | MedInstruct-test 216 | 1.26 | 2.1 | 1.87 | 1.34 | 1.88
124  Mistral-v0.1 7B_LFT | Alpaca 1K | just-eval-instruct 1k | 4.24 | 4.79 | 4.19 | 3.51 | 3.62
125  Mistral-v0.1 7B_SFT | Alpaca 1K | just-eval-instruct 1k | 1.13 | 2.6 | 1.49 | 1.1 | 1.42
126  Mistral-v0.1 7B_LFT | Alpaca 10K | just-eval-instruct 1k | 4.28 | 4.87 | 4.23 | 3.38 | 3.55
127  Mistral-v0.1 7B_SFT | Alpaca 10K | just-eval-instruct 1k | 1.11 | 2.34 | 1.33 | 1.1 | 1.22
128  Mistral-v0.1 7B_LFT | Alpaca 25K | just-eval-instruct 1k | 4.3 | 4.86 | 4.25 | 3.5 | 3.67
129  Mistral-v0.1 7B_SFT | Alpaca 25K | just-eval-instruct 1k | 1.01 | 1.75 | 1.13 | 1 | 1.03
130  Mistral-v0.1 7B_LFT | Alpaca 52K | just-eval-instruct 1k | 4.3 | 4.88 | 4.29 | 3.43 | 3.58
131  Mistral-v0.1 7B_SFT | Alpaca 52K | just-eval-instruct 1k | 1.04 | 1.77 | 1.2 | 1.03 | 1.09
132  Phi-1.5 1.3B_LFT | databricks-dolly 15k | just-eval-instruct 1k | 3.00 | 3.78 | 3.25 | 1.92 | 2.33
133  Phi-1.5 1.3B_SFT | databricks-dolly 15k | just-eval-instruct 1k | 2.37 | 3.15 | 2.51 | 1.69 | 2.14
134  Phi-1.5 1.3B_LFT | databricks-dolly 15k | MedInstruct-test 216 | 3.83 | 4.59 | 4.38 | 2.53 | 2.79
135  Phi-1.5 1.3B_SFT | databricks-dolly 15k | MedInstruct-test 216 | 3.09 | 3.87 | 3.45 | 2.08 | 2.58
136  Phi-1.5 1.3B_LFT | LIMA 1K | just-eval-instruct 1k | 3.98 | 4.46 | 3.93 | 3.85 | 3.82
137  Phi-1.5 1.3B_SFT | LIMA 1K | just-eval-instruct 1k | 3.71 | 3.49 | 3.18 | 3.39 | 3.25
138  Phi-1.5 1.3B_LFT | LIMA 1K | MedInstruct-test 216 | 4.56 | 4.85 | 4.54 | 3.55 | 3.26
139  Phi-1.5 1.3B_SFT | LIMA 1K | MedInstruct-test 216 | 4.35 | 4.7 | 3.97 | 3.36 | 3.34
140  Phi-1.5 1.3B_LFT | MedInstruct 1K | MedInstruct-test 216 | 4.32 | 4.26 | 4.33 | 4.29 | 4.16
141  Phi-1.5 1.3B_SFT | MedInstruct 1K | MedInstruct-test 216 | 3.52 | 3.74 | 3.44 | 3.31 | 3.23
142  Phi-1.5 1.3B_LFT | MedInstruct 10K | MedInstruct-test 216 | 4.34 | 4.27 | 4.39 | 4.31 | 4.19
143  Phi-1.5 1.3B_SFT | MedInstruct 10K | MedInstruct-test 216 | 3.72 | 3.84 | 3.55 | 3.44 | 3.42
144  Phi-1.5 1.3B_LFT | MedInstruct 25K | MedInstruct-test 216 | 4.39 | 4.28 | 4.44 | 4.33 | 4.21
145  Phi-1.5 1.3B_SFT | MedInstruct 25K | MedInstruct-test 216 | 3.87 | 3.92 | 3.74 | 3.52 | 3.43
146  Phi-1.5 1.3B_LFT | MedInstruct 52K | MedInstruct-test 216 | 4.41 | 4.32 | 4.51 | 4.69 | 4.53
147  Phi-1.5 1.3B_SFT | MedInstruct 52K | MedInstruct-test 216 | 4.15 | 4.11 | 4.10 | 4.24 | 4.30
148  Phi-1.5 1.3B_LFT | Alpaca 1K | just-eval-instruct 1k | 4.07 | 4.85 | 4.06 | 3.4 | 3.64
149  Phi-1.5 1.3B_SFT | Alpaca 1K | just-eval-instruct 1k | 3.19 | 4.42 | 3.31 | 2.72 | 3.25
150  Phi-1.5 1.3B_LFT | Alpaca 10K | just-eval-instruct 1k | 4.05 | 4.84 | 4.02 | 3.26 | 3.54
151  Phi-1.5 1.3B_SFT | Alpaca 10K | just-eval-instruct 1k | 3.13 | 4.13 | 3.23 | 2.67 | 3.24
152  Phi-1.5 1.3B_LFT | Alpaca 25K | just-eval-instruct 1k | 4.03 | 4.79 | 4.02 | 3.29 | 3.54
153  Phi-1.5 1.3B_SFT | Alpaca 25K | just-eval-instruct 1k | 3.04 | 4.49 | 3.11 | 2.64 | 3.28
154  Phi-1.5 1.3B_LFT | Alpaca 52K | just-eval-instruct 1k | 4.07 | 4.81 | 4.03 | 3.33 | 3.56
155  Phi-1.5 1.3B_SFT | Alpaca 52K | just-eval-instruct 1k | 3.93 | 4.2 | 3.65 | 3.88 | 3.49

Table 15: Results table 3

Models | Train Set | Test Set | Coding | Info-Seek | Math | Procedure | Reasoning | Role-Play | Writing
---|---|---|---|---|---|---|---|---|---
156  gpt-4-1106-preview | - | just-eval-instruct 1k | 4.67 | 4.78 | 4.57 | 4.87 | 4.80 | 4.76 | 4.76
157  LLaMa-2 7B-chat-hf | - | just-eval-instruct 1k | 3.31 | 3.73 | 2.54 | 4.07 | 3.93 | 3.94 | 4.07
158  LLaMa-2 13B-chat-hf | - | just-eval-instruct 1k | 3.27 | 4.02 | 2.84 | 4.22 | 4.09 | 4.11 | 4.32
159  LLaMa-2 70B-chat-hf | - | just-eval-instruct 1k | 4.02 | 4.41 | 3.67 | 4.76 | 4.56 | 4.45 | 4.81
160  LLaMa-2 7B_LFT | databricks-dolly 15k | just-eval-instruct 1k | 2.38 | 2.73 | 1.81 | 2.53 | 2.55 | 2.15 | 2.36
161  LLaMa-2 7B_SFT | databricks-dolly 15k | just-eval-instruct 1k | 1.78 | 2.32 | 1.62 | 2.21 | 2.43 | 2.46 | 2.43
162  LLaMa-2 7B_LFT | LIMA 1K | just-eval-instruct 1k | 2.37 | 2.78 | 1.72 | 2.87 | 2.68 | 2.65 | 2.63
163  LLaMa-2 7B_SFT | LIMA 1K | just-eval-instruct 1k | 1.76 | 2.48 | 1.25 | 2.45 | 2.58 | 2.05 | 2.3
164  LLaMa-2 7B_LFT | Alpaca 1K | just-eval-instruct 1k | 2.47 | 3.11 | 1.95 | 3.0 | 3.1 | 2.72 | 2.97
165  LLaMa-2 7B_SFT | Alpaca 1K | just-eval-instruct 1k | 1.79 | 2.34 | 1.51 | 2.4 | 2.45 | 2.06 | 2.27
166  LLaMa-2 7B_LFT | Alpaca 10K | just-eval-instruct 1k | 2.45 | 3.17 | 1.99 | 2.99 | 3.13 | 2.86 | 3.1
167  LLaMa-2 7B_SFT | Alpaca 10K | just-eval-instruct 1k | 1.75 | 2.26 | 1.53 | 2.23 | 2.5 | 2.29 | 2.69
168  LLaMa-2 7B_LFT | Alpaca 25K | just-eval-instruct 1k | 2.57 | 3.17 | 2.09 | 3.02 | 3.13 | 2.96 | 3.13
169  LLaMa-2 7B_SFT | Alpaca 25K | just-eval-instruct 1k | 1.88 | 2.36 | 1.51 | 2.41 | 2.58 | 2.52 | 2.74
170  LLaMa-2 7B_LFT | Alpaca 52K | just-eval-instruct 1k | 2.74 | 3.21 | 2.09 | 3.17 | 3.15 | 2.97 | 3.3
171  LLaMa-2 7B_SFT | Alpaca 52K | just-eval-instruct 1k | 1.54 | 2.15 | 1.41 | 2.28 | 2.39 | 2.3 | 2.48
172  LLaMa-2 7B_LFT | Tulu-V2-Mix 1K | just-eval-instruct 1k | 2.66 | 3.39 | 2.01 | 3.29 | 3.44 | 2.92 | 3.12
173  LLaMa-2 7B_SFT | Tulu-V2-Mix 1K | just-eval-instruct 1k | 1.61 | 2.2 | 1.37 | 2.33 | 2.37 | 2.11 | 2.18
174  LLaMa-2 7B_LFT | Tulu-V2-Mix 10K | just-eval-instruct 1k | 2.58 | 3.46 | 2.14 | 3.38 | 3.54 | 3.23 | 3.34
175  LLaMa-2 7B_SFT | Tulu-V2-Mix 10K | just-eval-instruct 1k | 1.85 | 2.45 | 1.3 | 2.57 | 2.84 | 2.54 | 2.79
176  LLaMa-2 7B_LFT | Tulu-V2-Mix 25K | just-eval-instruct 1k | 2.69 | 3.53 | 2.15 | 3.41 | 3.60 | 3.37 | 3.52
177  LLaMa-2 7B_SFT | Tulu-V2-Mix 25K | just-eval-instruct 1k | 1.95 | 2.43 | 1.31 | 2.56 | 2.84 | 2.58 | 2.86
178  LLaMa-2 7B_LFT | Tulu-V2-Mix 50K | just-eval-instruct 1k | 2.82 | 3.6 | 2.16 | 3.45 | 3.67 | 3.48 | 3.6
179  LLaMa-2 7B_SFT | Tulu-V2-Mix 50K | just-eval-instruct 1k | 2.1 | 2.41 | 1.30 | 2.38 | 2.83 | 2.67 | 2.91
180  LLaMa-2 7B_LFT | Tulu-V2-Mix 150K | just-eval-instruct 1k | 2.76 | 3.6 | 2.1 | 3.6 | 3.64 | 3.48 | 3.60
181  LLaMa-2 7B_SFT | Tulu-V2-Mix 150K | just-eval-instruct 1k | 2.19 | 2.53 | 1.46 | 2.66 | 2.93 | 2.84 | 3.04
182  LLaMa-2 7B_LFT | Tulu-V2-Mix 326k | just-eval-instruct 1k | 2.92 | 3.63 | 2.13 | 3.57 | 3.73 | 3.6 | 3.67
183  LLaMa-2 7B_SFT | Tulu-V2-Mix 326k | just-eval-instruct 1k | 2.32 | 2.81 | 2.05 | 2.80 | 3.04 | 2.96 | 3.21
184  LLaMa-2 7B_LFT | filtered-dolly 3k | just-eval-instruct 1k | 2.31 | 2.79 | 2.24 | 2.65 | 2.67 | 2.24 | 2.45
185  LLaMa-2 7B_SFT | filtered-dolly 3k | just-eval-instruct 1k | 1.7 | 2.05 | 1.46 | 1.99 | 2.15 | 1.97 | 2.02
186  LLaMa-2 7B_LFT | Evol-Instruct 70k | just-eval-instruct 1k | 2.78 | 3.56 | 2.08 | 3.55 | 3.66 | 3.49 | 3.76
187  LLaMa-2 7B_SFT | Evol-Instruct 70k | just-eval-instruct 1k | 2.23 | 2.68 | 1.73 | 2.77 | 3.05 | 2.98 | 3.19
188  LLaMa-2 7B_LFT | LIMA-Simple 1k | just-eval-instruct 1k | 2.53 | 2.97 | 1.9 | 2.85 | 2.96 | 2.7 | 2.87
189  LLaMa-2 7B_SFT | LIMA-Simple 1k | just-eval-instruct 1k | 1.78 | 2.29 | 1.39 | 2.09 | 2.51 | 2.25 | 2.28
190  LLaMa-2 7B_LFT | chatgpt 9k | just-eval-instruct 1k | 2.82 | 3.53 | 2.93 | 3.28 | 3.35 | 3.52 |
191  LLaMa-2 7B_SFT | chatgpt 9k | just-eval-instruct 1k | 2.58 | 3.13 | 1.82 | 3.01 | 3.15 | 2.72 | 2.93
192  LLaMa-2 7B_NFT | Alpaca 52K | just-eval-instruct 1k | 1.55 | 2.37 | 1.62 | 2.36 | 2.65 | 2.46 | 2.58

Table 16: Results table 3

Models | Train Set | Test Set | Coding | Info-Seek | Math | Procedure | Reasoning | Role-Play | Writing
---|---|---|---|---|---|---|---|---|---
193  LLaMa-2 13B_LFT | databricks-dolly 15k | just-eval-instruct 1k | 2.6 | 2.8 | 2.1 | 2.79 | 2.78 | 2.36 | 2.58
194  LLaMa-2 13B_SFT | databricks-dolly 15k | just-eval-instruct 1k | 1.93 | 2.5 | 1.46 | 2.19 | 2.58 | 1.97 | 2.39
195  LLaMa-2 13B_LFT | LIMA 1K | just-eval-instruct 1k | 2.45 | 2.97 | 2.08 | 2.95 | 2.78 | 2.48 | 2.83
196  LLaMa-2 13B_SFT | LIMA 1K | just-eval-instruct 1k | 1.6 | 2.32 | 1.34 | 2.23 | 2.45 | 1.97 | 2.22
197  LLaMa-2 13B_LFT | Alpaca 1K | just-eval-instruct 1k | 2.78 | 3.25 | 2.26 | 3.24 | 3.25 | 2.98 | 3.27
198  LLaMa-2 13B_SFT | Alpaca 1K | just-eval-instruct 1k | 1.86 | 2.44 | 1.67 | 2.27 | 2.55 | 2.11 | 2.49
199  LLaMa-2 13B_LFT | Alpaca 10K | just-eval-instruct 1k | 2.78 | 3.28 | 2.46 | 3.29 | 3.3 | 3.09 | 3.39
200  LLaMa-2 13B_SFT | Alpaca 10K | just-eval-instruct 1k | 1.87 | 2.33 | 1.51 | 2.32 | 2.52 | 2.42 | 2.8
201  LLaMa-2 13B_LFT | Alpaca 25K | just-eval-instruct 1k | 2.75 | 3.26 | 2.55 | 3.33 | 3.26 | 3.16 | 3.36
202  LLaMa-2 13B_SFT | Alpaca 25K | just-eval-instruct 1k | 1.65 | 2.17 | 1.45 | 2.12 | 2.36 | 2.1 | 2.57
203  LLaMa-2 13B_LFT | Alpaca 52K | just-eval-instruct 1k | 2.73 | 3.29 | 2.41 | 3.3 | 3.26 | 2.95 | 3.49