# TarGEN: A Simple Lightweight Method to Generate Synthetic Data using Large
Language Models

Himanshu Gupta1 Kevin Scaria1♢ Ujjwala Anantheswaran1♢∗ Shreyas Verma2
Mihir Parmar1 Saurabh Arjun Sawant1 Chitta Baral1 Swaroop Mishra1†
1Arizona State University 2Georgia Institute of Technology
{hgupta35<EMAIL_ADDRESS>
# TarGEN: Targeted Data Generation with Large Language Models

Himanshu Gupta1 Kevin Scaria1♢ Ujjwala Anantheswaran1♢∗ Shreyas Verma2
Mihir Parmar1 Saurabh Arjun Sawant1 Chitta Baral1 Swaroop Mishra1†
1Arizona State University 2Georgia Institute of Technology
{hgupta35<EMAIL_ADDRESS>
###### Abstract

The rapid advancement of large language models (LLMs) has sparked interest in
data synthesis techniques, aiming to generate diverse and high-quality
synthetic datasets. However, these synthetic datasets often suffer from a lack
of diversity and added noise. In this paper, we present TarGEN, a multi-step
prompting strategy for generating high-quality synthetic datasets utilizing a
Large Language Model. An advantage of TarGEN is its seedless nature; it does
not require specific task instances, broadening its applicability beyond task
replication. We augment TarGEN with a method known as self-correction
empowering LLMs to rectify inaccurately labeled instances during dataset
creation, ensuring reliable labels. To assess our technique’s effectiveness,
we emulate eight tasks from the SuperGLUE benchmark and finetune various
language models, including encoder-only, encoder-decoder, and decoder-only
models on both synthetic and original training sets. Evaluation on the
original test set reveals that models trained on datasets generated by TarGEN
perform $\sim 1-2\%$ points better than those trained on original datasets
(82.84% via synthetic vs. 81.12% on original using Flan-T5). When
incorporating instruction tuning, the performance increases to 84.54% on
synthetic data vs. 81.49% on original data by Flan-T5. A comprehensive
analysis of the synthetic dataset compared to the original dataset reveals
that the synthetic dataset demonstrates similar or higher levels of dataset
complexity and diversity. Furthermore, the synthetic dataset displays a bias
level that aligns closely with the original dataset. Finally, when pre-
finetuned on our synthetic SuperGLUE dataset, T5-3B yields impressive results
on the OpenLLM leaderboard, surpassing the model trained on the Self-Instruct
dataset by $4.14\%$ points. We hope that TarGEN can be helpful for quality
data generation and reducing the human efforts to create complex
benchmarks111https://github.com/kevinscaria/TarGEN
$*$ Currently in Microsoft $\dagger$ Currently in Google DeepMind
$\diamondsuit$ Equal Contribution .

##  1 Introduction

Large Language models (LLMs) like ChatGPT, Llama, Claude (Touvron et al.,
2023a; b) have showcased impressive results across a plethora of tasks (Müller
et al., 2019; OpenAI, 2023; Brown et al., 2020; Ouyang et al., 2022). As LLM
capabilities advance, the tools to test the extent of these capabilities
become insufficient (Liu et al., 2022b; He et al., 2023; Valmeekam et al.,
2022; Chen et al., 2021). This is particularly true for domain-specific
datasets, as the creation of expertly curated evaluation benchmarks is time
and labor-intensive (Clark et al., 2018; Suzgun et al., 2022; Wang et al.,
2022; Gupta et al., 2023b; 2021). However, the process of benchmark creation
can be accelerated with LLMs using only expertly provided task definitions and
with few to no examples. Several synthetic dataset creation methods such as
Self-Instruct (Wang et al., 2023), AttrPrompt (Yu et al., 2023a) and ZeroGen
(Ye et al., 2022a) have been proposed primarily for text classification tasks.
Moreover, certain proposed approaches also depend on seed samples (Wang et
al., 2023; Yu et al., 2023b), which serve as exemplars in prompts for LLMs.
These LLMs employ in-context learning to generate synthetic data points that
resemble the seeds, thereby inherently constraining their capacity to produce
diverse examples.

Figure 1: An overview of using TarGEN to generate instances for the WiC task.
We first create a set of prompts (1,2 in figure) to generate instance seeds,
or linguistic components unique to each task instance. In this case, the
instance seeds refer to homonyms (1) and their definitions (2). Next, we
create label-specific prompts (3) that generate instances based on instance
seeds and the relationship implied by the label for this task. Given an
instance seed, we generate TRUE instances by generating sentence pairs that
contain the word in the same sense. We generate FALSE instances by generating
sentence pairs containing the instance seed in different word senses. We use
zero-shot LLM inference to generate an initial set of synthetic instances. The
instances are then passed to our self-correction module consisting of a single
meta-prompt that is augmented with task instructions and evaluation examples,
and an LLM into which we pass synthetic instances with this prompt. This
allows us to re-label mislabeled data instances, helping us reduce noise.
Hence, based on the task description, we obtain high-quality synthetic
instances to evaluate a task.

To mitigate the aforementioned issues, we introduce TarGEN, a multi-step
prompting strategy (Fig 1). Using TarGEN, we can create high-quality diverse
datasets with little to no noise and accurate labels. This approach has the
additional advantage of not requiring any existing task instances as seeds for
generation. Furthermore, TarGEN features a unique module, self-correction,
that identifies and corrects mislabeled instances. We carry out the TarGEN
strategy in 4 key steps. We initialize a set of contexts to inject semantic
diversity, followed by the generation of task-specific elements we call
”instance seeds” - linguistic elements that form the unique basis of each
instance. These seeds can be sentences, passages, or more atomic elements but
are not input exemplars. For each “instance seed”, we formulate a label-
constrained that uses these seeds to generate a data instance attributable to
the constrained label. Finally, we leverage our evaluator model for self-
correction over the generated instances, and re-label them wherever necessary
- thus reducing noise and improving overall quality (For details, refer to
§A).

To demonstrate the method’s effectiveness, we create a synthetic variant of
the SuperGLUE (Wang et al., 2019) dataset using ChatGPT222We use gpt-3.5-turbo
in our experiments.. We train a variety of models from different families
(encoder only, encoder-decoder, and decoder only) on the synthetically
generated train set and the original SuperGLUE train set and evaluate these
models on the original test set. We find that models trained on the synthetic
train set perform at par as opposed to models trained on the original train
set ($\sim 1-2\%$ improvement across all models). Instruction tuning results
in a $3.42\%$ increase for Flan T5 models and a $3.24\%$ improvement for
Pythia GPT models (See detailed results in §3.1). We also conduct a comparison
between our dataset and Self-Instruct (Wang et al., 2023) by pre-finetuning
T5-3B (Raffel et al., 2020) on both datasets separately and evaluating them on
the OpenLLM benchmark (Clark et al., 2018; Zellers et al., 2019; Hendrycks et
al., 2021; Lin et al., 2022). Our findings indicate that T5-3B prefinetuned on
our dataset outperforms the model trained on Self-Instruct by $4.14\%$ points
($47.48\%$ using synthetic SuperGLUE vs $43.34\%$ using Self-Instruct).

We perform an in-depth analysis of the datasets which reveals the robustness
of TarGEN datasets in terms of dataset difficulty, diversity, and bias. It
exhibits comparable or higher dataset difficulty, as indicated by lower
$\mathcal{V}$-usable information (Ethayarajh et al., 2022), showcasing the
complexity of the datasets. Furthermore, our dataset has comparable lexical
diversity (Yu et al., 2023a) and consistently displays lower cosine similarity
between intra-data text pairs, highlighting the dataset’s rich and distinct
content. In terms of bias, our dataset aligns closely with the original
dataset, demonstrating a balanced representation of categories such as
Geopolitical Entities (GPE), Nationalities/Religious/Political Groups (NORP),
and Products such as Xbox-1, Airbus A380, Twitter (PRODUCT). A detailed
analysis is present in §4.

##  2 TarGEN

In this section, we formulate the problem statement and explain the data
generation pipeline. Also, we describe the datasets we intend to recreate from
scratch. Finally, we provide a detailed view of our task-specific generation
process.

###  2.1 Problem Formulation

A dataset is a set of unique data points that share common characteristics.
Given a dataset for a language task $t$, its data points can be expressed as
($d$,$l$) such that there exists a function
$f:\mathcal{D}\rightarrow\mathcal{L}$

$f_{t}(d)=l,\forall d\in\mathcal{D},l\in\mathcal{L}$ (1)

where $f_{t}$ is a mathematical representation of the task $t$,
$d\in\mathcal{D}$ is the instance input, and $l$ is the instance label from
$\mathcal{L}$, the label space for the given task. We formalize dataset
generation as a sequence of label-constrained text generation problems, where
the generation of an instance is constrained by its label value. This allows
us to control label distribution in our synthetic dataset and craft high-
quality instances by clearly defining the relationships between instance and
label. This circumvents the need for any seed instances from the original
dataset; i.e. for any given task, a dataset can be generated from scratch by
formulating its generation function from the task description. The task-
specific label-constrained dataset generation approach is given below:

$\bigcup\limits_{l\in\mathcal{L}}\bigcup\limits_{n=1}^{N_{l}}(G_{l,t,n}(l,i_{n}),L=l)$
(2)

where $N_{l}$ is the number of samples for the label $l$, $t$ is the task, and
$G_{l,t}$ is an inverse function such that $f_{t}$($G_{l,t}$($l$) ) = $l$, and
$i_{n}$ is the nth instance seed. Formulating these task- and label-specific
prompting strategies forms the crux of our simplified data synthesis pipeline.
While these individual prompts are task-specific, the nature of these prompts,
and the sequence they occur in, follow a framework engineered to create
diversity and improve coverage. The stages of this framework are as follows:

#### Step 1

We generate a set $\mathcal{C}$ of “contexts”, or“settings” that provide
unique semantic scope, such as “geopolitical news”, “book review”, or “movie
script”. These provide contexts within which a model can simulate a naturally
occurring linguistic excerpt, with the aim of maintaining semantic diversity
and avoiding repetition or overlap.

#### Step 2

We generate a set of passages, sentences, or task-specific elements, which we
call “instance seeds”. These seeds form the linguistic basis for each instance
of the task.

#### Step 3

We use the instance seeds as inputs to the generation prompt. This prompt is a
descriptive formulation of the inverse generation function $G_{l,t}$ allowing
us to generate data instances for the task.

#### Step 4

We pass all generated data instances through a single-step self-correction
process, where we use a task-specific prompt to reinforce the task
instructions and identify and correct any mislabeled instances. This helps
reduce noise and improve overall dataset quality.

###  2.2 Task-specific prompting strategies

We study the pattern of each task and create multi-step prompting strategies
tailored to each task. These strategies are detailed below. The generation
function $G_{l,t}$ for each task is included in §A. We generate a common set
of contexts (Step 1) for almost all tasks.

Datasets | Task Type | Instances Split
---|---|---
Axg | NLI | Not Ent: 146 Ent: 138
Boolq | Bin. Class. | True: 2535 False: 1764
CB | NLI | Cont:119 Ent:115 Neut:16
Copa | Bin. Class. | Choice 1: 195 Choice 2: 107
Record | MCQ | 1778 MCQs
RTE | NLI | Not Ent: 1241 Ent: 1249
Wic | Bin. Class. | True: 2433 False: 2410
Wsc | Bin. Class. | True: 259 False: 285

Table 1: Statistics of the SuperGLUE dataset. Following abbreviations are used
Bin. Class: Binary Classification, Ent: Entailment, Cont: Contradiction, NLI:
Natural Langauge inference

#### Dataset Statistics:

We choose the following tasks from the SuperGLUE benchmark: 1\. CommitmentBank
(CB). 2. Choice of Plausible Alternatives (COPA). 3. Recognizing Textual
Entailment (RTE). 4. Word-in-Context (WiC). 5. Winograd Schema Challenge
(WSC). 6. BoolQ. 7. Reading Comprehension with Commonsense Reasoning (ReCoRD).
and 8. Winogender Diagnostics (AX-g). Given the challenges posed by each
dataset, we employ TarGEN to create synthetic instances that can be used to
evaluate language model performance. Table 1 showcases the instances split
used in the original and synthetic datasets. Since label generation is
controlled, synthetic SuperGLUE was created to match the exact number of
original instances, while maintaining a balanced label distribution333Due to
ChatGPT budget constraint, ReCoRD dataset was truncated to have 1778
instances, BoolQ dataset had 4299 instances and MultiRC dataset was skipped..
The exact data synthesis pipeline and prompts used for each dataset can be
found in §A.

CB (De Marneffe et al., 2019) tests the ability to resolve the relationship
between the premise and hypothesis which is a clause-embedding predicate under
an entailment canceling operator. For each relationship label
$l\in\\{entailment,neutral,contradiction\\}$, and based on a given context
$c\in\mathcal{C}$, we generate pairs of sentences that share the relationship.

COPA (Roemmele et al., 2011) is an open-domain commonsense reasoning dataset
used to evaluate a model’s understanding of relative causal inference by
identifying the more likely hypothesis. We generate instances for CAUSE and
EFFECT relations. In this case, Step 3 involves generating a premise and 2
hypotheses for a given context. For each relationship $r$, we generate (1)
sentence pairs $(P,C)$ such that the premise and hypothesis share the
relationship specified, and (2) an alternate hypothesis $C_{alt}$ which
explicitly does not share the specified relationship with the premise $P$.
Thus $(P,C)\in r,(P,C_{alt})\notin r$. The label space for this task is
defined as $\mathcal{L}=\\{C1,C2\\}$. To ensure an even label split, we
alternatively select attribute instances of $C$ as $C1$ and $C_{alt}$ as $C2$
and vice versa and set the instance labels as $C1$ and $C2$, respectively.

RTE is a collection of textual entailment challenges. For this task, step 2
consists of generating a set of premises $\mathcal{P}$ as instance seeds. For
each $p\in\mathcal{P}$, we then generate hypotheses that are either logically
sound $(l=entailment)$ or logically unsound, i.e. do not follow the premise
$(l=notentailment)$.

Figure 2:  Matrices showing the effect of the self-correction step across
various datasets of SuperGLUE. The row values show the number of labels that
were originally assigned to that label (ent, non: entailment, non-entailment;
neutr, contr: neutral, contradiction). The number in a cell $(i,j)$ reflects
the number of labels originally assigned to label $i$ which were re-labeled to
label $j$ after self-correction. While the majority of the instances had their
labels reaffirmed by self-correction, a significant number of instances were
re-labeled as a result of this step.

WiC (Pilehvar & Camacho-Collados, 2019) formulates semantic disambiguation as
a binary classification task on two sentences with a common noun or verb. We
forgo a list of contexts for this task. Instead we curate a list of homonyms
($\mathcal{S}$) along with all possible definitions $(\mathcal{M}_{s}\forall
s\in\mathcal{S})$ for each word. These words acts as instance seeds. For each
$m\in\mathcal{M}_{s}$, given the label $True$, we generate a pair of sentences
$(d1,d2)$ containing the word $s$, such that the definition of $s$ in $d1$ and
$d2$ is $m$. For the label $False$, we randomly choose $m1,m2\in M_{s},m1\neq
m2$ and generate $d1,d2$ such that the definitions of $s$ in $d1$ and $d2$ are
$m1$ and $m2$ respectively, making them distinct in word sense.

WSC (Levesque et al., 2012) is a dataset that evaluates coreference resolution
based on a given pair of pronoun and noun phrase. For each context, we
generate pairs of distinct noun phrases $(N_{1},N_{2})$ with identical
plurality. This pronoun and noun phrases act as instance seeds. For each pair
$(N1,N2)$, we then generate text $s$ containing $N1$ and $N2$ with all
pronouns labeled with coreferred noun phrases. We randomly select an
ambiguously-coreferent pronoun $P$ from the text. Based on this pronoun and
the label constraint, we affix a noun phrase to the input instance.

BoolQ (Clark et al., 2019) question answering dataset tests understanding
entailment relations between multiple concepts. In Step 2, we generate
passages with multiple entities and inter-entity relations which act as
instance seeds. In Step 3, we generate a query, based on the passage
$p\in\mathcal{P}$ and the label constraint $l\in\\{Yes,No\\}$. In the case of
$l=Yes$, the query is information that can be inferred from the passage. For
$l=No$, we generate a query that is directly contradicted by the passage.

ReCoRD (Zhang et al., 2018) dataset evaluates understanding implied entailment
relationships. In Step 2, we generate a set of passages $\mathcal{A}$ to act
as instance seeds. Next, for each article $a\in\mathcal{A}$, we generate a
complex, context-relevant sentence, and subsequently obscure a single entity
reference.

AX-g (Rudinger et al., 2018) aims to quantify the extent of gender bias by
evaluating accuracy of pronoun coreference. In Step 2, we generate 10 subject
pairs. For each subject pair, we then generate an independent clause
containing these subjects. These independent clauses are then used to generate
dependent clauses coreferent with each subject, to act as instance seeds. In
step 3, we use these subject-specific dependent clauses and the subject pairs
to generate gender-agnostic hypotheses based on label constraints.

###  2.3 Self-correction

Despite learning capabilities, LLMs demonstrate inconsistent reasoning (Ye &
Durrett, 2022). We remedy them by implementing self-correction, an evaluation
strategy that corrects inconsistent labels in the data synthesis process. We
leverage an LLM as an evaluator model (ChatGPT in this case) to verify the
alignment between the generated instances and their labels, as well as the
alignment between these instances and the task description. This is achieved
by utilizing the LLM’s existing knowledge and in-context learning abilities,
given relevant validation instances. Self-correction consists of a single
meta-prompt that is common to all tasks. The task instructions and task-
specific validation examples are used to augment the meta-prompt and tailor it
to each generated dataset. This meta-prompt, and the task instructions are in
§C.

Based on the provided input, the meta-prompt helps evaluate the correctness of
its attributed label. If this label is deemed not correct, the evaluator model
generates the correct label based on the instructions and instance input. This
helps refine the quality of the generated data instances and significantly
reduces noise. We show the effects of this step on the label distributions for
categorically-labeled datasets in Fig 2. For low-complexity tasks i.e.
requiring simple logical inferences, we observe that the number of correctly
generated labels surpasses the number of labels that need to be corrected. For
high-complexity tasks, i.e. tasks requiring advanced language understanding,
LLMs fall prey to long chains of thought and fallacious reasoning. AX-g and
WSC are two such tasks. Fig 2 shows a significant number of instances require
relabeling - demonstrating the necessity of self-correction.

| Axg | Boolq
---|---|---
| Og | Syn | Og-I | Syn-I | Og | Syn | Og-I | Syn-I
Cerebras | 73.23 | 76.00 | 72.80 | 77.63 | 84.12 | 86.11 | 84.77 | 88.61
Pythia | 74.90 | 78.43 | 74.57 | 79.80 | 83.29 | 84.02 | 82.61 | 85.05
T5 | 77.12 | 79.14 | 77.65 | 79.84 | 84.43 | 85.64 | 84.16 | 86.87
Flan | 78.03 | 80.23 | 76.20 | 81.70 | 84.43 | 86.01 | 85.08 | 88.21
RoBERTa | 77.10 | 78.17 | 76.35 | 82.14 | 84.43 | 84.56 | 84.07 | 87.56
| Copa | RTE
Cerebras | 80.56 | 81.98 | 80.01 | 82.11 | 81.12 | 83.25 | 81.02 | 85.42
Pythia | 79.98 | 80.74 | 80.54 | 83.27 | 82.98 | 83.65 | 82.85 | 87.87
T5 | 82.12 | 83.34 | 82.32 | 85.91 | 86.18 | 86.10 | 86.79 | 88.92
Flan | 81.78 | 82.41 | 82.36 | 86.24 | 88.91 | 90.19 | 89.86 | 89.22
RoBERTa | 82.12 | 83.98 | 80.99 | 83.16 | 88.20 | 89.01 | 88.32 | 88.84
| CB | Record
Cerebras | 88.21 | 89.15 | 88.60 | 88.93 | 67.88 | 68.12 | 68.42 | 71.45
Pythia | 91.93 | 89.63 | 89.74 | 93.03 | 68.83 | 69.19 | 68.61 | 73.36
T5 | 90.32 | 92.43 | 89.84 | 92.05 | 71.13 | 72.23 | 70.44 | 72.07
Flan | 89.02 | 93.21 | 88.06 | 94.32 | 70.21 | 70.11 | 71.58 | 76.83
RoBERTa | 87.86 | 90.12 | 92.36 | 90.48 | 69.88 | 70.21 | 70.29 | 70.02
| Wic | Wsc
Cerebras | 66.78 | 68.72 | 66.70 | 70.22 | 82.12 | 85.28 | 86.31 | 90.66
Pythia | 68.33 | 71.77 | 70.53 | 70.84 | 86.71 | 87.32 | 86.44 | 88.09
T5 | 68.01 | 71.13 | 68.35 | 69.41 | 85.56 | 85.95 | 85.91 | 88.43
Flan | 70.12 | 72.45 | 69.29 | 71.43 | 86.23 | 88.13 | 88.08 | 88.35
RoBERTa | 69.90 | 70.06 | 70.16 | 71.23 | 87.21 | 85.65 | 82.37 | 83.21

Table 2: Performance of various models over SuperGLUE tasks. For each dataset,
we compare the performance of these models (Cerebras, Pythia, T5, FLAN,
RoBERTa) when trained on 4 distinct variants: Og (original train data), Syn
(synthetic train data), Og-I (inst. tuning original train data), Syn-I (inst.
tuning synthetic train data). For each task, we denote the highest performing
model trained on original train data in green, and the highest performing
model trained on synthetic train data in blue. All results are presented in %.

##  3 Experiments and Results

We train five models in single task learning (STL) setting where we finetune
each model with original and synthetic dataset separately. We evaluate them on
the original test set to measure performance. The above experiments are
repeated in instruction tuning settings as well (Gupta et al., 2023a). We also
perform a multi-task learning (MTL) experiment where a T5-3B is finetuned on
all original and synthetic datasets separately in a multi-task fashion (Mishra
et al., 2021a). The results in §3.1 are the average of five runs.

Models: Following models are used: RoBERTa Large (354M) (Liu et al., 2019),
Pythia GPT (410M) (Biderman et al., 2023), Cerebras GPT (590M) (Dey et al.,
2023), Flan T5 Large (780 M) (Chung et al., 2022), T5 Large (780M) (Raffel et
al., 2020) and T5-3B in MTL setting.

Hyperparameters: We use 6xNvidia Tesla P40 GPU. Batch Size: 16 for STL and 1
MTL setting. Gradient Accumulation Steps: 1, Learning rate: 5e-5, Num of
Epochs: 5 for STL and 1 MTL setting.

Evaluation Metric: Following the SuperGLUE benchmark, we use accuracy for all
dataset tasks in the benchmark.

###  3.1 Results

| Og | Syn | Og-I | Syn-I
---|---|---|---|---
Cerebras | 78.64 | 79.83 | 78.80 | 81.88
Pythia | 79.48 | 80.59 | 79.49 | 82.66
T5 | 80.72 | 82.00 | 80.83 | 82.94
Flan | 81.12 | 82.84 | 81.49 | 84.54
RoBERTa | 79.98 | 81.47 | 80.08 | 82.08

Table 3: Average performance over all tasks, for each model and the variant of
data it. Og and Syn represents Original and Synthetic dataset. I represents
Instruction tuning.

Table 2 showcases the results of all the models trained on both individually
on original and synthetic dataset. In the traditional finetuning setting, the
best synthetic datasets score are nearly same or slightly higher compared to
original datasets. However, there is a considerable jump of $\sim 3\%$ in
performance when instruction tuning (Mishra et al., 2021b; Wei et al., 2021;
Scaria et al., 2023) is used. Table 3 gives the average model wise results for
the same. Table 4 denotes the results when the datasets are trained in
multitask fashion on T5-3B model. Since we use a larger model, the results are
significantly better than single task learning results. Flan T5 gets the
biggest jump from 81.12 avg scores in original dataset to 84.54 in synthetic
with instructions. This could be attributed to the degree of instruction
tuning flan has gone through. RoBERTa has lowest jumps between score with just
avg. 79.98 in original to 82.08 in synthetic with instructions.

Dataset | Boolq | Wic | CB | Axg | Record | RTE | Wsc | Copa
---|---|---|---|---|---|---|---|---
Original | 89.02 | 68.42 | 84.54 | 41.66 | 68.52 | 87.72 | 72.11 | 94.65
Synthetic | 90.04 | 74.78 | 93.22 | 49.13 | 76.59 | 92.41 | 73.07 | 95.20

Table 4: Results using a T5-3B model trained in a multi-task fashion.
Original: Using a combined set of original datasets to train the model.
Synthetic: Using synthetic versions to train the model. All numbers are in %.
We measure performance in accuracy, except in the case of ReCoRD, where we use
the Rouge-L score. Higher score is highlighted. We find that model trained on
synthetic data is significantly better than the one trained on original data.

##  4 Analysis

In this section, we present a comprehensive analysis of the synthetic data
generated, examining both quantitative and qualitative aspects.

#### Lexical Dataset Diversity:

We analyze the dataset diversity, along the lines presented in (Yu et al.,
2023a). We initiate our exploration with a straightforward vocabulary-based
examination to assess lexical diversity, as summarized in Table 5. Notably,
our TarGEN synthetic data exhibits an average lexical diversity that is 25%
higher across various dataset tasks.

#### Semantic Dataset Diversity:

To analyze the semantic dataset diversity, we examine the cosine similarity
distribution of SentenceBERT embeddings of within-dataset sample pairs as
presented in Fig 3. Across most SuperGLUE tasks, the TarGEN datasets
consistently display lower cosine similarity than the original dataset,
indicating reduced semantic similarity of within-dataset samples and,
consequently, higher semantic diversity. This observation underscores our
approach’s intrinsic capability to generate diverse samples. Moreover, our
findings of higher cosine similarity of the original datasets align with those
of (Parmar et al., 2023), where the authors highlight the propensity for
crowdsourced datasets to exhibit high bias and low diversity. This phenomenon
arises as crowdsourced workers often adhere to patterns provided by dataset
creators. The TarGEN generated dataset therefore has the capacity to generate
samples with enhanced diversity.

Figure 3: Comparison of semantic diversity across datasets among the original
and the synthetically generated dataset. It can be seen that the original
datasets’ cosine similarity is higher for most tasks as compared to the
synthetic datasets’ which has a consistently lower cosine similarity
indicating higher semantic diversity. Figure 4:  Comparison of PVI
($\mathcal{V}$-usable information) across datasets for the original dataset
and the synthetically generated dataset. Synthetic data seems to have better
quality as Original datasets PVI is concentrated around -0.1 to 0.1 whereas
the synthetic data generated has a diverse mix of difficulty level among the
samples. Figure 5: Comparison of dataset bias for the BoolQ dataset and the
synthetically generated BoolQ dataset.

#### Dataset Difficulty:

Conventionally dataset difficulty is gauged by comparing state-of-the-art
model performance against human performance, relying on performance gaps to
infer difficulty. However, this approach lacks granularity at the sample level
and doesn’t elucidate which attributes are informative for the model. To
address this, we use $\mathcal{V}$-usable information (Ethayarajh et al.,
2022), which estimates the information an input $X$ holds for predicting the
target $Y$ across a family of models $\mathcal{V}$. Lower $\mathcal{V}$-usable
information indicates higher dataset difficulty for $\mathcal{V}$. Fig. 4
offers a comparative view of dataset difficulty between the original and
synthetically generated datasets by TarGEN. Notably, the synthetic datasets
exhibit a diverse range of samples with varying pointwise $\mathcal{V}$-usable
information, showcasing their diversity in terms of difficulty. Furthermore,
the absence of mislabelled samples, indicated by positive $\mathcal{V}$-usable
information in the synthetically generated datasets, underscores the
effectiveness of self-correction prompts.

Dataset | boolq | wic | cb | axg | records | rte | wsc | copa
---|---|---|---|---|---|---|---|---
Original | 251.8k | 65.8k | 6.4k | 3.2k | 160.3k | 79.1k | 8.8k | 5.1k
Synthetic | 190.6k | 76.4k | 16.6k | 4.4k | 236.7k | 49.4k | 8.9k | 5.2k

Table 5: Lexical diversity of the dataset. Figures in red correspond where
diversity of synthetic dataset is limited. Blue correspond where the synthetic
dataset has more diversity than original.

| T5SSG | T5SI | T5AP
---|---|---|---
ARC | 41.48 | 39.17 | 40.56
HellaSwag | 59.43 | 55.23 | 57.25
MMLU | 38.11 | 36.76 | 38.25
TruthFulQA | 50.88 | 42.19 | 48.69
Average | 47.48 | 43.34 | 46.19

Table 6: Comparison of Synthetic SuperGLEU, Self Instruct Dataset, and
AttrPrompt. T5-3B pre-finetuned on both datasets individually and finetuned on
OpenLLM datasets.

#### Dataset Bias:

We conducted a comprehensive analysis of dataset bias, evaluating both the
original dataset and the generated dataset. Using this method, we visualize
the distribution of tokens related to named entities, belonging to
geopolitical entities (GPE), Products, and Nationalities or religious or
political groups (NORP). The distribution of input tokens for the original and
synthetically generated BoolQ dataset is presented in Fig. 5. Notably, TarGEN
was not explicitly designed to mitigate bias restrictions but rather to
generate a dataset that closely aligns with the original dataset while
circumventing the use of seed data. Consequently, our results reveal that the
distribution of GPE, Product, and NORP entities in the original dataset
closely resembles that of the TarGEN dataset. Detailed plots for other
datasets can be found in §D 444To ensure a broad-scale examination across all
samples, we utilized the Spacy library and its $en\\_core\\_web\\_sm$ model to
extract named-entity tags..

#### Comparison with Self-Instruct:

To compare the synthetic SuperGLUE dataset with other synthetic instruction
following benchmarks, we choose Self-Instruct (Wang et al., 2023) and
AttrPrompt (Yu et al., 2023a), popular synthetic dataset generation
frameworks. We choose T5-3B and prefinetune using synthetic SuperGLUE we call
T5SSG. We do the same with Self-Instruct and AttrPrompt datasets to get T5SI
and T5AP respectively. All models are now finetuned on train sets of MMLU,
HellaSwag and ARC train set in a multitask fashion and evaluated on OpenLLM
benchmark. Table 6 showcases the results for the benchmark. T5SSG performs
4.14% than T5SI underscoring the quality of targeted dataset generation by the
TarGEN framework over Self-Instruct.

##  5 Related Work

Recent research has witnessed the emergence of various methods harnessing LLMs
as synthetic data generators. Specifically, in the context of few-shot
classification tasks where labeled data is scarce, several approaches have
been introduced. SuperGEN (Schick & Schütze, 2021) and ZeroGEN (Meng et al.,
2022) leverage LLMs to produce synthetic data. For zero-shot tasks, SunGen
(Gao et al., 2023) and ProGen employ noise-filtering techniques, while ProGEN
(Ye et al., 2022b) utilizes model feedback to ensure generated data quality.
Similarly, (Chia et al., 2022) introduces structured prompts for tasks like
relation triplet extraction. Moreover, (Liu et al., 2022a) and (Wiegreffe et
al., 2022) propose synthetic data generation methods for natural language
entailment (NLI) tasks and free-text explanations in a human-AI collaborative
setting. There also have been approaches proposed to generate tabular data
(Borisov et al., 2023) and instruction data (Peng et al., 2023; Sun et al.,
2023).

The prevailing research direction in synthetic data generation predominantly
focuses on zero/few-shot classification or entails fine-tuning (Chen et al.,
2023) or iterative fine-tuning of open-source LLMs (Yu et al., 2023a). In
contrast, our method is simple, lightweight, and adaptable even to closed-
source LLMs like ChatGPT. It also does not rely on labeled examples.
Furthermore, our approach uses a multi-step prompting strategy alongside self-
correction to do targeted data generation and uphold data generation quality
in terms of diversity, bias, noise, and mislabelling. Finally, existing
dataset generation methods often are limited by reliance on seed tasks from
the original dataset (Wang et al., 2023). We circumvent this bottleneck by
proposing a seedless pipeline that leverages high-level dataset
characteristics for the generation process.

##  6 Conclusion

In this work, we introduced TarGEN, a multi-step prompting strategy for
generating high-quality and diverse synthetic datasets utilizing LLMs without
any human supervision. We described a step-by-step methodology for TarGEN to
synthesize a dataset from instructions without any task exemplars. To evaluate
our proposed framework, we emulated eight tasks from the SuperGLUE benchmark
and compared it with the original SuperGLUE by training different families of
models. Experimental results reveal that models fine-tuned on our synthetic
SuperGLUE outperform models fine-tuned on the original SuperGLUE. A
comprehensive analysis of synthetic benchmark w.r.t. original benchmark
resulted in several interesting findings such as data instances in our
synthesized benchmark are more difficult and diverse compared to the original
benchmark, and also exhibit similar dataset bias. Further comparison with
Self-Instruct and AttrPrompt revealed that synthetic SuperGLUE served as
better pre-finetuning corpora when evaluated on the OpenLLM benchmark
resulting in an impressive performance using T5-3B.

Though TarGEN facilitates high-quality data generation, we believe that it is
important to assess our proposed frameworks within a multi-lingual context and
also on additional benchmarks, including BigBench, LILA, and HELM.
Furthermore, TarGEN currently relies on the ChatGPT model for synthesizing the
benchmark, but our future plans involve exploring the impact of other LLMs
such as GPT-4, Llama-2, and Falcon when employed with TarGEN. We believe that
TarGEN can serve as a valuable tool for enhancing the quality of data
generation, thus reducing human effort.

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## Appendix

##  Appendix A Task-specific instance generation pipelines

This section details the task-specific LDG strategy used to generate
instances.

###  A.1 COPA

Step 1:

Generate a list of domains or settings in which events can take place.

Step 2:

Generate N sentences describing events that could take place in the domain
[DOMAIN].

Step 3:

query = CAUSE. Add ”What was the CAUSE of this?” to the premise during post-
processing For the given sentence, generate 2 hypotheses (Hypothesis 1,
Hypothesis 2) , such that
Hypothesis 1 is a probable cause of the sentence.
Hypothesis 2 is very unlikely to be the cause of the sentence.
Example: Sentence: I cast a long shadow.
Hypothesis 1: The sun was low in the sky.
Hypothesis 2: The grass was tall.
Explanation: Hypothesis 1, the low position of the sun is more likely to cause
a long shadow. The height of the grass has nothing to do with the long shadow,
and thus is unlikely to be a cause.
Sentence: [SENTENCE]

query = RESULT. Add ”What was the RESULT of this?” to the premise during post-
processing
For the given sentence, generate 2 hypotheses (Hypothesis 1, Hypothesis 2) ,
such that
Hypothesis 1 is a probable result of the sentence.
Hypothesis 2 is very unlikely to be the result of the sentence.
Example: Sentence: I fell down the stairs.
Hypothesis 1: I injured myself.
Hypothesis 2: My mother bought a new car.
Explanation: Hypothesis 1, the injury is more likely to be a result of the
fall. The buying of a car is not implied by the sentence which talks about
falling down stairs - hence it is less likely to be the result of the
sentence.
Sentence: [SENTENCE]

Mathematical formulation of Step 3:

$G_{l,t,r}(l,r=result)=(P,C1,C2)\colon\begin{cases}P\implies
C1,P\centernot\Rightarrow C2&l=C1\\\ P\implies C2,P\centernot\Rightarrow
C2&l=C2\\\ \end{cases}$ (3)
$G_{l,t,r}(l,r=cause)=(P,C1,C2)\colon\begin{cases}C1\implies
P,C2\centernot\Rightarrow P&l=C1\\\ C2\implies P,C1\centernot\Rightarrow
P&l=C2\\\ \end{cases}$ (4)

The relationship $r\in\\{cause,result\\}$ is affixed to the premise $P$ as a
query. The generated triplet $(P,C1,C2)$ and the constrained label
$l\in\\{C1,C2\\}$ that answers the query, form an instance of this dataset.

Self-correction

Instructions:
You are given a premise and 2 possible hypotheses (Choice 1 and Choice 2) as
input. Select the hypothesis which is more likely to have a causal link to the
sentence.
If the premise asks for CAUSE: If the premise is more likely to be the result
of Choice 1, output Choice 1. Otherwise, output Choice 2.
If the premise asks for RESULT: If Choice 1 is more likely to be the result of
the premise, output Choice 1. Otherwise, output Choice 2.

###  A.2 CommitmentBank

Step 1:

Generate a list of domains or settings in which events can take place.

Step 2: N/A

Step 3:

label = entailment
For the given domain [DOMAIN], generate [N] pairs of sentences (Sentence 1,
Sentence 2) such that Sentence 2 logically follows, or is implied by, Sentence
1.
Example:
Sentence 1: The singer was very nervous.
Sentence 2: The singer saw critics in the front row.
Now generate N such sentence pairs. Generated sentences:

label = neutral
For the given domain [DOMAIN], generate [N] pairs of sentences (Sentence 1,
Sentence 2) such that Sentence 2 has no relation with Sentence 1, and cannot
be derived from it.
Example:
Sentence 1: I made coffee.
Sentence 2: My assignment is due tomorrow.
Now generate N such sentence pairs. Generated sentences:

label = contradiction
For the given domain [DOMAIN], generate [N] pairs of sentences (Sentence 1,
Sentence 2) such that Sentence 2 is explicitly contradicted by Sentence 1.
Example:
Sentence 1: The musician was on time for the gig.
Sentence 2: The promoter praised the musician for being on time.
Now generate N such sentence pairs. Generated sentences:

Mathematical formulation of Step 3:

$G_{l,t}(l)=(P,H)\colon\begin{cases}P\implies H&l=entailment\\\
P\centernot\Rightarrow H&l=neutral\\\ P\implies\neg
H&l=contradiction\end{cases}$ (5)

Each generated pair $(P,H)$ of premise and hypothesis, along with its
constrained label $l$ $\in$ $\\{entailment,neutral,contradiction\\}$,
constitutes a synthetic instance for this dataset.

Self-correction

Instructions:
The input contains a premise and a hypothesis. Assume the premise is always
true.
1\. If the hypothesis logically follows the premise and the hypothesis can be
derived from the information in the premise, Output “entailment”
2\. If the hypothesis directly contradicts information in the premise, Output
“contradiction”
3\. If the hypothesis is unrelated to the premise, or cannot be sufficiently
proven from the information in the premise, Output “neutral”

###  A.3 MultiRC

Step 1:

List of categories: News* ; Wikipedia* ; History and anthropology* ; Society,
law, and justice* ; Elementary school science textbooks* ; 9/11 reports* ;
Fiction - literature or movie plots* ; art ; computer science ; biological
processes ; physics ; chemistry ; linguistics ; psychiatry and psychology ;
supernatural phenomena (* - present in original dataset)

Step 2:

Generate 50 unique topics or titles in the category [CATEGORY] Generate 7
short paragraphs on the following topic: [TOPIC]

Step 3:

Given a paragraph, frame a question that requires information from multiple
sentences of the paragraph to be answered correctly. Then, generate a set of
options. The correct answer may be a combination of one, some, or all options.
Also include options that do not answer the above question. Finally, output
the combination of options that form the correct answer.
Paragraph: [PARAGRAPH]

Mathematical formulation of Step 3: Not applicable in this case

Self-correction NA

Instructions: You are given a passage followed by a question and a list of
options. Based on information in the passage, output all the options that can
answer the question. The output must not include options that do not answer
the question. The output must not lack any options that answer the question.

###  A.4 RTE

Step 1:

Generate a list of topics or domains to talk about.

Step 2:

For the given domain [DOMAIN], generate N complex sentences containing
information relevant to this domain.

Step 3:

label = entailment
Given a sentence as premise, add a logically sound hypothesis concerning the
information in the premise.
Premise: A place of sorrow after Pope John Paul II died became a place of
celebration as Roman Catholic faithful gathered in downtown Chicago to mark
the installation of new Pope Benedict XVI. Hypothesis: Benedict XVI is the new
Catholic Pope.
Premise: [SENTENCE]
label = non-entailment
Given a sentence as premise, add a logically unsound hypothesis concerning the
information in the premise.
Premise: A place of sorrow after Pope John Paul II died became a place of
celebration as Roman Catholic faithful gathered in downtown Chicago to mark
the installation of new Pope Benedict XVI. Hypothesis: Benedict XVI died
recently. Premise: [SENTENCE]

Mathematical formulation of Step 3:

$G_{l,t,p}(l,p)=(P=p,H)\colon\begin{cases}P\implies H&l=True\\\
P\centernot\Rightarrow H&l=False\\\ \end{cases}$ (6)

The generated premise and hypothesis pairs $(P,H)$, as well as their
associated label $l\in\\{True,False\\}$ form the binary labeled instances for
this task.

Self-correction

Instructions: You are given a premise and a hypothesis. If the hypothesis
logically follows from the premise, output entailment. If the hypothesis
cannot be logically derived from the information in the premise, output not
entailment.

###  A.5 WSC

Step 1:

Generate a list of domains or settings in which events can take place and
where people can interact.Examples: ’A restaurant, ’a museum’, ’a lively rock
concert’, ‘An opera house’

Step 2:

Identify a pair of subjects (Subject 1, Subject 2) in the context of [DOMAIN].
For example, in the context of “a classroom”, a pair of subjects could be
(Subject1 : teacher, Subject 2 : student), or (Subject1: students, Subject2:
rivals). For the given domain, generate [10] such subject pairs. For 5 pairs,
Both subjects must be singular For 5 pairs, both subjects must be plural. Both
subjects must be humans or groups of humans. Output all 10 pairs in a numbered
list.

Step 3:

For a given subject pair (Subject 1, Subject 2), generate 2 passages, S1 and
S2. Each passage must be 2 sentences or fewer. You are also given the gender
of both subjects. For every pronoun in the sentences, identify which subject
is being referred to.
Example:
Input:
Subject 1: Teacher, Subject 2: Student
Pronouns: He/him
Output:
S1: The teacher was disappointed in the student because [he=teacher] had high
hopes for [him=student].
S2: The teacher and the student are not on good terms. [He=student] is very
rebellious, and does not show up to classes.
Explanation: In S1: It is clear that the student has disappointed the teacher,
who had high hopes for the student. Hence, “he had high hopes” - “he” refers
to the teacher, “for him” - “him” refers to the student. In S2: The student is
rebellious and does not show up to classes - hence the “he” in “he is very
rebellious” refers to the student
Input:
Subject 1: [SUBJECT 1] Subject 2: [SUBJECT 2]
Pronouns: (randomly chosen)
Output:

Mathematical formulation of Step 3:

$G_{l}=\begin{cases}(s,N1,P)\colon N1\leftrightarrow P&l=True\\\
(s,N2,P)\colon N2\leftrightarrow P&l=True\\\ (s,N1,P)\colon N2\leftrightarrow
P&l=False\\\ (s,N2,P)\colon N2\leftrightarrow P&l=False\\\ \end{cases}$ (7)

where $s$ contains $N1$, $N2$, and $P$. We denote coreference with the
$\leftrightarrow$ operator.

Self-correction

Instructions: The given input talks about 2 noun phrases (Subject 1 and
Subject 2). You are given Subject 1 and a Pronoun that occurs in the input.
Rules:
1\. If the pronoun refers to the noun phrase Subject 1, the task output is
TRUE.
2\. If the pronoun refers to the noun phrase Subject 2, the task output is
FALSE.

###  A.6 BoolQ

Step 1:

Generate a list of domains to write an article in.

Step 2:

Generate 50 unique topics or titles in the category [DOMAIN] Generate 7 short
paragraphs on the following topic: [TOPIC]

Step 3:

label = TRUE
You are given a passage. Generate a boolean query. The answer to this query,
based on the passage, must be YES.
Example:
Passage: The Millennium Falcon, a legendary starship piloted by Han Solo and
Chewbacca, has become an iconic symbol of rebellion and hope in the struggle
against the oppressive Galactic Empire. May the Force be with you, as the epic
adventures of Luke Skywalker, Princess Leia, and Darth Vader remind us that
even in the darkest times, there is always a glimmer of light and a chance for
redemption in the Star Wars universe.
Query: Does Han Solo work with Chewbacca? Answer: YES, Han Solo and CHewbacca
pilot the Falcon together.
Similarly, generate a query for the following passage.
Passage: [PASSAGE]
Query:
label = FALSE
You are given a passage. Generate a boolean query. The answer to this query,
based on the passage, must be NO.
Example:
Passage: The Millennium Falcon, a legendary starship piloted by Han Solo and
Chewbacca, has become an iconic symbol of rebellion and hope in the struggle
against the oppressive Galactic Empire. May the Force be with you, as the epic
adventures of Luke Skywalker, Princess Leia, and Darth Vader remind us that
even in the darkest times, there is always a glimmer of light and a chance for
redemption in the Star Wars universe.
Query: Does Han Solo work alone? Answer: NO, Han Solo works with Chewbacca
Similarly, generate a query for the following passage.
Passage: [PASSAGE]
Query:

Mathematical formulation of Step 3:

$G_{l,t,p}(l,p)=(P=p,Q)\colon\begin{cases}P\implies Q&l=Yes\\\ P\implies\neg
Q&l=No\\\ \end{cases}$ (8)

Self-correction

Instructions: You are given a passage followed by a question. The questions
ask for confirmation of a fact that may or not be present in the passage. If
the passage explicitly confirms the fact being asked in the question, output
TRUE as the answer to the question. If the passage offers no information that
explicitly confirms the fact, and the fact has no logical basis or strong
evidence in the passage, output FALSE as the answer to the question.

###  A.7 ReCoRD

Step 1:

Generate a list of domains to write an article in.

Step 2:

Generate 50 unique topics or titles in the category [DOMAIN] Generate 7 short
paragraphs on the following topic: [TOPIC]

Step 3:

Example:
textbf: As a spring breeze wafted into his trench French commander Georges
Lamour saw something surreal drift his way - a yellow-green cloud. ’All my
trenches are choked,’ he cried into the field telephone to headquarters. ’I am
falling myself!’ Chlorine gas — carried by favourable winds over Flanders
Fields from German positions — had been used for the first time. It was April
22, 1915. Scroll down for video Chlorine gas — carried by favourable winds
over Flanders Fields from German positions — sowed terror and agony for the
first time on April 22, 1915. Above, German Red Cross workers carry bottles of
water to help revive troops. German forces launched first attack using gas on
April 22, 1915. 150,000 tons of gas were used by German and Allied forces in
WW1.
Query: Had they been able to peer a bit further across no-man’s land they
would have seen how [X] troops had dug in, under cover of night, more than
5,000 gas cylinders with tubes pointing their way. Answer: German
Explanation: The query fits the context of WW1 and talks about both entities
involved in the war. The paragraph mentions German forces using gas. German is
the entity replaced by [X] in the query.
Instruction: Generate a complex sentence that fits the context of the given
paragraph.
The generated query must be a statement about the events in the paragraph. Put
[X] in place of any one entity mention. The query must not contain any events
mentioned in the paragraph. The answer must contain the entity mention that
can replace [X].
textbf: [textbf]
Query:

Mathematical formulation of Step 3:

$G_{a,t}(a)=(A=a,s,e)\colon s,e\in a,l=e$ (9)

where $a$ is the article, and $s,e$ are the paraphrased sentence (with one
entity $e$ obscured) as well as the entity $e$ obscured acting as the label.

Self-correction

Instructions: You are given a passage, followed by a query. The query contains
[X] in place of any one entity mentioned. Output the entity that could
logically replace [X] in the query.

###  A.8 AXg

Step 1:

Generate a list of domains or settings in which events can take place and
where people can interact.Examples: ’A restaurant, ’a museum’, ’a lively rock
concert’, ‘An opera house’

Step 2:

Identify a pair of subjects (Subject 1, Subject 2) in the context of [DOMAIN].
For example, in the context of “a classroom”, a pair of subjects could be
(Subject1 : teacher, Subject 2 : student), or (Subject1: students, Subject2:
rivals). For the given domain, generate [10] such subject pairs. For a given
pair of subjects, Generate 4 sentences with the following specifications:
Initial clause: A clause containing Subject 1 and Subject 2
Sentence 1: Initial clause with a dependent clause containing a gendered
pronoun. Dependent clause should refer to Subject 1, not Subject 2.
Sentence 2: Completely identical to sentence 1 but with a different gendered
pronoun
Sentence 3: Initial clause with a dependent clause containing a gendered
pronoun. Dependent clause should refer to Subject 2, not Subject 1.
Sentence 4: Identical to sentence 3 but with a different gendered pronoun
Ensure the dependent clauses in Sentence 1 and Sentence 2 refer to Subject 1
and Subject 2 respectively. Sentences 1 and 2 must be identical in terms of
dependent, with nothing but the pronoun changed. Similarly, Sentences 3 and 4
must be identical, with only the pronoun changed. Sentences 1 to 4 must start
with the same clause.
Only one gendered pronoun must be present in the dependent clauses. All
sentences should make logical sense. Now generate 4 sentences according to
these specifications for the following subjects.
Subject 1: [SUBJECT 1] Subject 2: [SUBJECT 2]

Step 3:

label = entailment
Given a sentence, (independent clause + dependent clause), and the subject
being referred to in its dependent clause: generate a sentence containing the
subject, which logically follows the sentence. This generated sentence should
have no gendered pronouns. label = non-entailment
Given a sentence, (independent clause + dependent clause), and the subject
being referred to in its dependent clause: generate a sentence containing the
subject, which does not logically follow the sentence. This generated sentence
should have no gendered pronouns.

Mathematical formulation of Step 3:

$G_{l,t}(s,i,d)=(i,d,h)\colon\begin{cases}(s,d)\implies h&l=entailment\\\
(s,d)\centernot\Rightarrow h&l=notentailment\\\ \end{cases}$ (10)

where $i$ and $d$ refer to the independent and dependent clauses respectively,
$s$ is the subject that the dependent clause is coreferent with, and $h$ is
the generated gender-agnostic hypothesis based on the subject $s$ and instance
label $l\in\\{entailment,notentailment\\}$.

Self-correction

Instructions: You are given a premise and a hypothesis. If the hypothesis
logically follows from the premise, output entailment. If the hypothesis
cannot be logically derived from the information in the premise, output not
entailment.

###  A.9 WiC

Step 1: N/A

Step 2:

Generate a list of 50 verbs or nouns which have more than one meaning. Given a
word [WORD], print a numbered list of 4 or fewer distinct definitions of the
word. Example:
Word : shoot
Definitions: 1\. to fire a bullet 2\. click a picture 3\. record on video 4\.
a movie set.
Word: [WORD]
Definitions:

Step 3:

label = TRUE
For given word and list of all possible definitions, print any one definition,
followed by 2 sentences containing the word in this definition.
Example:
Word: key
Definitions:
1\. a piece of shaped metal used to open or close a lock 2\. a button or lever
on a keyboard or musical instrument 3\. a crucial or central element 4\. to
provide something with a key or identifying code
Chosen definition: 1\. a piece of shaped metal used to open or close a lock
Sentences:
1\. I lost my key yesterday 2\. He shouldn’t steal people’s keys.
Word: [WORD]
Definitions: [DEFINITIONS]
Chosen definition:
label = FALSE
Given a word and a list of definitions: For each definition, print a sentence
containing the word in that definition.
Example:
Word: key
Definitions:
1\. a piece of shaped metal used to open or close a lock 2\. a button or lever
on a keyboard or musical instrument 3\. a crucial or central element 4\. to
provide something with a key or identifying code
Sentences:
1\. I lost my key yesterday
2\. This key on the piano is out of tune.
3\. The key to victory is planning ahead.
4\. I don’t know what to key in to gain access.
Explanation:
1\. I lost my [key] yesterday - here [key] means 1. a piece of shaped metal
used to open or close a lock
2\. This [key] on the piano is out of tune. - here [key] means 2. a button or
lever on a keyboard or musical instrument
3\. The [key] to victory is planning ahead. - here [key] means 3. a crucial or
central element
4\. I don’t know what to [key] in to gain access. - here [key] means 4. to
provide something with a key or identifying code
Word: [WORD]
Definitions: [DEFINITIONS]
Sentences:

Mathematical formulation of Step 3:

$G_{l,t}(l)=(d_{1},d_{2})\colon\begin{cases}f(s,d_{1})=f(s,d_{2})&l=True\\\
f(s,d_{1})\neq f(s,d_{2})&l=False\end{cases}$ (11)

where $s\in\mathcal{S}$ and $f(s,d_{1})=m\in\mathcal{M}_{s}$ is the word sense
of $s$ in the context of $d_{1}$.

Self-correction

Instructions: You are given a word (Keyword) and 2 sentences, both containing
the Keyword.
1\. If the definition of the Keyword in both sentences is almost the same,
print FALSE
2\. If the Keyword means something different in sentence 1 than in sentence 2,
print TRUE.

##  Appendix B Prompts for instruction tuning

#### CommitmentBank

The input contains a premise and a hypothesis.
1\. If the hypothesis logically follows the premise and the hypothesis can be
derived from the information in the premise, Output “entailment”
2\. If the hypothesis directly contradicts information in the premise, Output
“contradiction”
3\. If the hypothesis is unrelated to the premise, or cannot be sufficiently
proven from the information in the premise, Output “neutral”

Example:
Premise: “There is no food in the larder, we never went shopping! We could
starve!”, she cried.
Hypothesis: She did not go shopping for food.
Output: entailment
Explanation: The speaker is complaining about the lack of food due to not
going shopping. The hypothesis is strongly supported by the information in the
premise. Hence the output is entailment, i.e. the premise entails the
hypothesis.

Premise: The diplomatic summit was a failure. The hostility between the
representatives led to a breakdown in the talks. The world watched as they
traded jibes and ended the meeting on bad terms.
Hypothesis: A diplomatic solution was reached through negotiation.
Output: contradiction
Explanation: The premise relates the failure of diplomatic talks, and implies
lack of negotiation, contradicting the hypothesis. Hence, the output is
contradiction.

Premise: “I hope the cat is well,” I said. I was lying, because I wished the
cat to be dead.
Hypothesis: The cat is dead.
Output: neutral
Explanation: The premise mentions the speaker wishes the cat was dead, but
says they hope it is alive. From the premise, no conclusion can be drawn about
the actual state of the cat. Since the premise cannot provide evidence to
confirm or deny the hypothesis, the output is neutral.

Premise: [PREMISE]
Hypothesis:

#### COPA

You are given a premise and 2 possible hypotheses (Choice 1 and Choice 2) as
input. Select the hypothesis which is more likely to have a causal link to the
sentence.
If the premise asks for CAUSE:
If the premise is more likely to be the result of Choice 1, output Choice 1.
Otherwise, output Choice 2.
If the premise asks for RESULT:
If Choice 1 is more likely to be the result of the premise, output Choice 1.
Otherwise, output Choice 2.

Example:
Premise : My body cast a shadow over the grass.What was the CAUSE of this?
Choice 1 :The sun was rising.
Choice 2 :The grass was cut.
Output : Choice 1
Explanation : The premise asks for CAUSE. Out of the 2 choices, choice 1
describing the position of the sun is more likely to cause a shadow. The grass
being cut has no relation.

Example:
Premise : The elderly woman suffered a stroke.What happened as a RESULT?
Choice 1 :The woman’s daughter came over to clean her house.
Choice 2 :The woman’s daughter moved in to take care of her.
Output : Choice 2
Explanation : The premise asks for RESULT. Choice 2 is more likely to
logically follow the premise of the sick elderly woman. There is nothing to
suggest cleaning the house.

Premise : [PREMISE]
Choice 1 : [CHOICE 1]
Choice 2 : [CHOICE 2]

#### MultiRC

You are given a passage followed by a question and a list of options. Based on
information in the passage, output all the options that can answer the
question.

Example:
Passage:
Bioluminescence is a captivating natural phenomenon that illuminates the
depths of our oceans and various terrestrial environments. It is the
mesmerizing ability of living organisms to produce light through chemical
reactions within their bodies. This enchanting light emission occurs primarily
in marine creatures such as glowing plankton, jellyfish, and deep-sea
creatures, turning the oceanic world into a dazzling light show. The
mesmerizing glow serves a range of purposes, from attracting prey and mates to
warding off predators. Bioluminescence not only adds a magical touch to the
hidden realms of the Earth but also remains an essential area of scientific
research, offering insights into evolutionary adaptations and potential
biomedical applications. Exploring the mysteries of bioluminescence continues
to unveil the secrets of these glowing organisms and further ignites our
curiosity about the wonders of the natural world.

Question: Which organisms demonstrate bioluminescence?
Options:
A) Glowing plankton
B) Deep-sea creatures
C) Terrestrial environments
D) Jellyfish
E) Land-based organisms
Output: A) Glowing plankton
B) Deep-sea creatures
D) Jellyfish

Passage:
[PARAGRAPH]

Question: [QUERY]
Options:
[LIST OF OPTIONS]
Output:

#### RTE

You are given a premise and a hypothesis. If the hypothesis logically follows
from the premise, output entailment. If the hypothesis cannot be logically
derived from the information in the premise, output not entailment.

Premise: My car ran out of diesel and I had to walk 6 miles to my house.
Hypothesis: My car needs diesel to run.
Output: entailment

Premise: The diplomacy talks mark a gradual lessening of tensions between the
UK and Argentina.
Hypothesis: Argentina and UK are heading towards a war.
Output: not entailment

Premise: [PREMISE]
Hypothesis: [HYPOTHESIS]
Output:

#### WiC

You are given a word (Keyword) and 2 sentences, both containing the Keyword.
1\. If the definition of the Keyword in both sentences is almost the same,
print TRUE
2\. If the Keyword means something different in sentence 1 than in sentence 2,
print FALSE.

Example:
Word: shoot
Sentence 1 : he shot the wedding with a handheld camera
Sentence 2 :he shot me with a gun Output:
FALSE

Example:
Word: shoot
Sentence 1 : the shoot was suspended due to the actor’s absence
Sentence 2 : the director wrapped the shoot up by evening. Output:
TRUE

Word: [WORD]
Sentence 1: [SENTENCE 1]
Sentence 2: [SENTENCE 2]
Output:

#### WSC

The given input talks about 2 noun phrases (Subject 1 and Subject 2). You are
given Subject 1 and a Pronoun that occurs in the input.
Rules:
1\. If the pronoun refers to the noun phrase Subject 1, output LABEL: TRUE.
2\. If the pronoun refers to the noun phrase Subject 2, output LABEL: FALSE.
Explain your reasoning.

Example:
Input: The city councilmen refused the demonstrators a permit because they
feared violence. Subject 1: The city councilmen. Pronoun: They.
Output: TRUE
Explanation:
Noun phrases: city councilmen (Subject 1) ; demonstrators (Subject 2)
The pronoun “they” occurs in the phrase “they feared violence”. Out of the 2
subjects, demonstrators are more likely to commit violence, and city
councilmen are more likely to fear the violence. This phrase must be talking
of the city councilmen (Subject 1). This follows Rule 1.

Example:
Input: The scientist studied the lion because he was paid to do so. Subject 1:
lion. Pronoun: he
Output: FALSE
Explanation:
Noun phrases: lion (Subject 1) ; scientist (Subject 2)
The pronoun “he” occurs in the phrase “he was paid to do so”. This is unlikely
to refer to the lion, as the scientist (Subject 2) is more likely to be paid
to study something. Thus, the pronoun must refer to Subject 2.

Input:
[INPUT]
Output:

#### BoolQ

You are given a passage followed by a question. The questions ask for
confirmation of a fact that may or not be present in the passage. If the
passage explicitly confirms the fact being asked in the question, output TRUE
as the answer to the question. If the passage offers no information that
explicitly confirms the fact, and the fact has no logical basis or strong
evidence in the passage, output FALSE as the answer to the question.

Passage:
In J. R. R. Tolkien’s Middle-earth, the Half-elven (Sindarin singular
Peredhel, plural Peredhil, Quenya singular Perelda) are the children of the
union of Elves and Men. Of these, the most significant were the products of
couplings between the Eldar (the Elves who followed the Call to Valinor) and
the Edain (the Men of the Three Houses of early Men who allied themselves with
the Eldar in their war against Morgoth).
Question:
can elves and humans mate lord of the rings Answer: TRUE
Explanation: The fact needing confirmation is whether elves and humans can
mate, in the context of Lord of the Rings,by J R R Tolkien. The passage
clearly mentions that in his universe, children of elves and humans or men
exist, thus proving that elves and humans can mate in this universe. Hence,
the answer to the question is TRUE

Passage:
Over the years, the Movie Maker has undergone various updates that added
improvements to the software. It even integrates with other tools to provide a
more advanced level of editing. However, it is still a basic video editing
app. If you are looking to create a more professional-looking movie, apps such
as Adobe Premiere Pro are your best option.
Question:
Is the movie maker app used by the film industry
Answer: FALSE Explanation: The question asks to confirm the fact whether the
movie maker app is used by the film industry. The passage clearly states that
movie maker is a basic video editing app. Furthermore, the passage mentions
Adobe Premiere Pro specifically as a professional tool. As any industry uses
professional tools, the passage clearly contradicts the fact presented in the
question. Hence, the answer is FALSE.

Passage: [PARAGRAPH]
Question:
[QUERY]
Answer:

#### ReCoRD

You are given a passage, followed by a query. The query contains [X] in place
of any one entity mentioned. Output the entity that could logically replace
[X] in the query.
Example:
Passage: Georges Lamour saw something drift his way - a yellow-green cloud.
’All my trenches are choked,’ he cried. Chlorine gas was carried by winds over
Flanders Fields from German positions. Chlorine gas sowed terror and agony for
the first time on April 22, 1915. Red Cross workers carried bottles of water.
German forces launched their first attack using gas on April 22, 1915. 150,000
tons of gas were used by German and Allied forces in WW1.
Query: Had they been able to peer a bit further, they would have seen how [X]
troops had dug in, under cover of night, more than 5,000 gas cylinders with
tubes pointing their way.
Output:
[X]: German

Passage:
Query: [QUERY]
Output:
[X]:

#### AXg

You are given a premise and a hypothesis. If the hypothesis logically follows
from the premise, output entailment. If the hypothesis cannot be logically
derived from the information in the premise, output not entailment.

Example:
Input:
Premise: The paralegal forgot to file the client’s paperwork, so he was fired.
Hypothesis: The client was fired.
Output: not entailment
Explanation: The paralegal made a mistake here, and clients are not likely to
be fired. Hence, the hypothesis does not make logical sense. Hence, output
“non-entailment”.

Example:
Input:
Premise: The athlete stole the cheerleader’s uniform so she could wear it to a
party.
Hypothesis: The athlete was going to a party.
Output: entailment
Explanation: Despite gender stereotypes, “she” here refers to the athlete, not
the cheerleader. According to the premise, the athlete stole the uniform so
that the athlete could wear it to a party, not the cheerleader, who does not
have the outfit any longer. The hypothesis that the athlete was strongly
evidenced by the premise. Hence, output “entailment”.

Input:
Premise: [PREMISE]
Hypothesis: [HYPOTHESIS]
Output:

##  Appendix C Framework for meta-prompt for self-correction

These are the “task instructions” you are given to accomplish a task:

insert task instructions here.

Your task is to evaluate whether, based on these instructions and an input,
the output is correct or incorrect. Also provide an explanation for your
reasoning.

insert task-specific self-correction samples here.

Now evaluate the input and output below based on task instructions, and print
whether the output is correct or incorrect. Remember to provide an explanation
for your evaluation. Remember, the actual answer is based on the input and the
task instructions, not the output.
Input:
Output:
Evaluation:

Task-specific instructions can be found in §B. Task-specific self-correction
examples follow the following schema:

For correct outputs

Actual result: correct output
Output: correct output
Based on this input and the given task instructions, the output is CORRECT.
Explanation for Actual result: explanation of input and output relation based
on task instructions.
Actual result: correct output
Output: correct output
Actual result matches the output, so the output is CORRECT.

For incorrect outputs

Actual result: correct output
Output: incorrect output
Based on this input and the given task instructions, the output is INCORRECT.
Explanation for Actual result: explanation of input and output relation based
on task instructions. Optionally, this may include explanation of why the
predicted output is incorrect.
Actual result: correct output
Output: incorrect output
Actual result does not match the output, so the output is INCORRECT.

##  Appendix D Extended Analysis

The section contains plots for dataset bias for other datasets.

Figure 6: Comparison of dataset bias for the CB dataset and the synthetically
generated CB dataset. Figure 7: Comparison of dataset bias for the Record
dataset and the synthetically generated Record dataset. Figure 8: Comparison
of dataset bias for the RTE dataset and the synthetically generated RTE
dataset. Figure 9: Comparison of dataset bias for the WIC dataset and the
synthetically generated WIC dataset.