# Attribute or Abstain: Large Language Models as Long Document Assistants

Jan Buchmann, Xiao Liu, Iryna Gurevych
Ubiquitous Knowledge Processing (UKP) Lab
Department of Computer Science and Hessian Center for AI (hessian.AI)
Technical University of Darmstadt
www.ukp.tu-darmstadt.de

###### Abstract

LLMs can help humans working with long documents, but are known to
hallucinate. Attribution can increase trust in LLM responses: The LLM provides
evidence that supports its response, which enhances verifiability. Existing
approaches to attribution have only been evaluated in RAG settings, where the
initial retrieval confounds LLM performance. This is crucially different from
the long document setting, where retrieval is not needed, but could help.
Thus, a long document specific evaluation of attribution is missing. To fill
this gap, we present LAB, a benchmark of 6 diverse long document tasks with
attribution, and experiment with different approaches to attribution on 4 LLMs
of different sizes, both prompted and fine-tuned.
We find that citation, i.e. response generation and evidence extraction in one
step, mostly performs best. We investigate whether the “Lost in the Middle”
phenomenon exists for attribution, but do not find this. We also find that
evidence quality can predict response quality on datasets with simple
responses, but not so for complex responses, as models struggle with providing
evidence for complex claims. We release code and data for further
investigation111Github repository, code under Apache 2.0, dataset licenses
depend on original license, see §B..

Attribute or Abstain: Large Language Models as Long Document Assistants

Jan Buchmann, Xiao Liu, Iryna Gurevych Ubiquitous Knowledge Processing (UKP)
Lab Department of Computer Science and Hessian Center for AI (hessian.AI)
Technical University of Darmstadt www.ukp.tu-darmstadt.de

##  1 Introduction

Figure 1: Long document assistants should attribute, i.e. provide responses
with evidence, or abstain. Example from QASPER Dasigi et al. (2021). Figure
requires emojis to display correctly. Figure 2: The approaches to attribution
in long document scenarios analyzed in this work.

Recent LLMs can process long documents Shaham et al. (2023); Li et al.
(2023b), showing great potential as long document assistants. For example
(Fig. 1), such an assistant could answer a researcher’s questions about a
paper. However, due to LLM hallucinations Slobodkin et al. (2023), the
researcher must verify responses, which is difficult with lengthy papers. To
improve verifiability and trust, the assistant should either attribute Rashkin
et al. (2023) or abstain Slobodkin et al. (2023): If it finds the necessary
information, it should provide a response and point to the evidence in the
paper (attribute). If not, it should clearly communicate this (abstain). We
investigate the capabilities of LLMs to fulfill these requirements, and the
relation between response quality and evidence quality.

Formally, we assume an instruction $I$ (e.g. a question) and a document $D$
consisting of segments $d$ (e.g. paragraphs)222In other scenarios, $D$ can be
a large corpus, see below.. There are two subtasks that can be solved jointly
or independently: One is to generate a response $R$ (e.g. an answer)
containing statements $r$. If the response is not abstained (e.g. by saying
“unanswerable”), the other subtask is to retrieve evidence $E_{i}\subset D$
for each $r_{i}$ such that $r_{i}$, is attributable to $E_{i}$, i.e.
"according to $E_{i}$, $r_{i}$" is true Rashkin et al. (2023).

Different approaches to attribution can be defined based on the subtask order
(Fig. 2): (1) post-hoc: an LLM generates a response $R$, and evidence is
retrieved from $D$ based on $R$. (2) retrieve-then-read: Evidence $E$ is
retrieved from $D$, and an LLM generates a response based on $E$. (3)
citation: Based on $D$, an LLM generates a response and retrieves evidence in
one step.
To decrease the input length in post-hoc and citation, $D$ can be reduced to
$D^{\prime}\subset D$ via and additional initial retrieval step. This results
in two further attribution approaches: (4) reduced-post-hoc and (5) reduced-
citation.

Attribution has only been investigated in retrieval augmented generation (RAG)
settings, where $D$ is not a document, but a large corpus (e.g. Wikipedia)
that does not fit the LLM context. This means that only approaches with
initial retrieval, retrieve-then-read, reduced-post-hoc and reduced-citation,
can be used. Performance depends on retrieval quality, and the best-performing
approach is unclear Bohnet et al. (2022); Gao et al. (2023b); Malaviya et al.
(2024).

In contrast, when $D$ is a long document that fits the LLM context, the
confounding initial retrieval can be omitted. Still, it is possible that
separating generation and retrieval improves performance, as shown in related
works on task decomposition Sun et al. (2023) and reducing long LLM inputs,
where Agrawal et al. (2024) found positive effects, while Xu et al. (2024) and
Bai et al. (2023) did not. These works did not consider attribution, so there
is a lack of knowledge on the effect of separating response generation and
evidence retrieval, and the optimal approach to attribution on long documents.

The document length poses additional challenges: Recent works have found that
LLM performance on long-input tasks depends on the position of the information
in the context Liu et al. (2024); Staniszewski et al. (2024); Ravaut et al.
(2024). Whether this can also be observed for attribution has not yet been
investigated.

Evidence quality (attributability) can be evaluated without external reference
Honovich et al. (2022); Yue et al. (2023); Tang et al. (2024). If evidence
quality were positively correlated with response quality, bad responses could
be abstained from by filtering responses with bad evidence quality. If not,
this would lead to abstaining from potentially helpful responses with
insufficient evidence. Current research on the relation of response quality
and evidence quality is inconclusive: Bohnet et al. (2022) and Gao et al.
(2023b) reranked multiple sampled responses by evidence quality. While the
former found an improvement in response quality, the latter did not. Neither
provide an analysis, so we lack understanding if and how evidence quality
correlates with response quality.

To close these gaps, we compile LAB, a Long-document Attribution Benchmark of
6 long-document datasets with diverse tasks (QA, classification, NLI and
summarization) and domains (science, law, governmental and Wikipedia). We
conduct experiments using the outlined attribution approaches on 4 LLMs of
varying sizes, prompted and fine-tuned, to answer three research questions:

RQ1: What are optimal approaches for attribution in long document tasks? We
find that large and fine-tuned models reach best evidence quality via
citation, but small prompted LLMs can benefit from post-hoc evidence
retrieval.

RQ2: Do LLMs exhibit positional biases in evidence attribution? Concerning
evidence retrieval, except for GovReport, we find no particular bias, as the
predicted and gold evidence distributions are mostly similar. However, we find
that response quality generally decreases as evidence appears later in the
document.

RQ3: What is the relation between evidence quality and response quality? We
find that evidence quality can predict response quality on datasets with
single-fact responses, but not so for multi-fact responses, as models struggle
with providing evidence for complex claims.

##  2 Related Work

##### Attribution

Current research in attribution is done in three strains: First, some works
evaluate post-hoc, retrieve-then-read and citation approaches in their ability
to produce attributed responses Bohnet et al. (2022); Liu et al. (2023), some
proposing new datasets Malaviya et al. (2024) or benchmarks DeYoung et al.
(2020a); Gao et al. (2023b). Second, methodological works propose new fine-
tuning Schimanski et al. (2024); Huang et al. (2024) and prompting Berchansky
et al. (2024); Fierro et al. (2024) methods to improve the citation
capabilities of language models, but do not compare to retrieve-then-read or
post-hoc approaches. Both of these strains have focused on open domain QA, and
neglected the long document scenario. Here, we close these gaps by providing a
comprehensive investigation of attribution for long documents.
To evaluate attributability automatically, most works have used TRUE,
Flan-T5-XXL fine-tuned on several NLI datasets Honovich et al. (2022); Bohnet
et al. (2022); Gao et al. (2023b); Fierro et al. (2024); Huang et al. (2024).
More recently, Attrscore Yue et al. (2023) and Minicheck Tang et al. (2024)
were proposed specifically for the evaluation of attributability. We compare
these models and employ the best-performing for attributability evaluation.

##### LLMs for long documents

While there is no universal definition of "long documents", existing long
document benchmarks contain documents of 1500 to 50000 words average length
Shaham et al. (2023); Dong et al. (2024); An et al. (2023); Li et al. (2023b).
Initial LLMs were limited to contexts of less than 2000 tokens Brown et al.
(2020); Touvron et al. (2023), but recent advances in hardware and efficiency
Dao (2023) have spurred the development of models with context $\geq$$8000$
tokens, e.g. Longchat Li et al. (2023a), Mistral Jiang et al. (2023),
GPT-3.5-16K 333https://platform.openai.com/docs/models/gpt-3-5-turbo or
GPT-4-Turbo-128K.444https://platform.openai.com/docs/models/gpt-4-turbo-and-
gpt-4 We add to this line of research by evaluating a range of models in their
attribution capabilities.

##  3 Methods

###  3.1 Datasets

| Domain | Task | #Inst Train/Dev/Test | Doc #W | Res $|R|$/#W | #Evi | Evi Lvl
---|---|---|---|---|---|---|---
QASPER (QSP) [1] | Science | QA | 2675/1005/1451 | 3937 | 1/12 | 1.7 | para
Natural Questions (NQ) [2] | Wiki | QA | 232191/6205/7307 | 4693 | 1/3 | 1 | para
Evidence Inference (EI) [3] | Science | Cls | 18545/1232/1218 | 3962 | 1/1 | 1.1 | para
Wice (WIC) [4] | Web | FC | 4234/349/358 | 1339 | 1/1 | 3.7 | sent
ContractNLI (CNLI) [5] | Legal | NLI | 7191/1037/2091 | 1697 | 1/1 | 1.5 | para
GovReport (GR) [6] | US Gov. | Sum | 15107/964/969 | 8464 | 20/517 | N/A | N/A
Table 1: The datasets in LAB span multiple domains and task types. The numbers
for Natural Questions differ from the original publication, as we filtered the
instances (§B). Column names: Doc #: Average number of words per document. Res
$|R|$/#W: Number of statements / Average number of words per response. #Evi:
Number of annotated evidence segments per instance. Evi Lvl: Level of
annotated evidence. Tasks: QA: Question answering. Cls: Classification. FC:
Fact checking. NLI: Natural Language Inference. Sum: Summarization. [1] Dasigi
et al. (2021) [2] Kwiatkowski et al. (2019) [3] DeYoung et al. (2020b) [4]
Kamoi et al. (2023) [5] Koreeda and Manning (2021) [6] Huang et al. (2021)

The datasets in LAB are shown in Table 1. All datasets are in English.
GovReport Huang et al. (2021) is the only dataset without annotated evidence.
To simulate gold evidence, we used BM25 to find the 2 best-matching paragraphs
from a document for each sentence in the gold summary similar to Ravaut et al.
(2024). Due to limited resources, we use at most 2000 test instances from any
dataset (100 for GPT-4). For details and examples see §B.

###  3.2 Evaluation

##### Response parsing

For all datasets except GovReport, responses consist of single statements. For
GovReport, we split responses into statements (sentences) using NLTK Bird
(2006). For citation, LLMs are expected to generate segment identifiers (“[1]
[2]”) at the end of each statement.

##### Evaluation of response quality

For comparability with related work, we used established metrics to evaluate
response quality: Exact match F1 (EM)555Found to correlate better than
BERTScore Zhang* et al. (2020) with human judgment in our preliminary
experiments. on QASPER Dasigi et al. (2021); Shaham et al. (2023) and Natural
Questions Kwiatkowski et al. (2019); Bohnet et al. (2022), classification
macro F1 (CF1) for Evidence Inference DeYoung et al. (2020b), Wice Kamoi et
al. (2023) and ContractNLI Koreeda and Manning (2021) and ROUGE-L666ROUGE has
shown good correlation with human relevance judgments in Wu et al. (2024).
(RL, Lin 2004) for GovReport Huang et al. (2021); Shaham et al. (2023).

##### Evidence F1 (EF1)

For datasets that define a fixed response vocabulary (i.e. Evidence Inference,
Wice and ContractNLI), we compute evidence quality as evidence F1, comparing
the predicted evidence with annotated ground truth evidence. If there is no
annotated evidence, evidence F1 is 1 if no evidence was predicted and
otherwise 0.

##### Attributability (ATT)

For the other datasets, evidence F1 is insufficient: GovReport does not come
with annotated evidence, and for datasets with free-form responses (QASPER and
Natural Questions), evidence F1 is too rigid: A model might produce a response
different from the ground truth, but supported by the retrieved evidence. Even
though evidence quality is high, evidence F1 might be low. For these datasets,
we evaluate evidence quality as attributability777Also known as citation
recall Gao et al. (2023b)., Gao et al. (2023b); Huang et al. (2021);
Schimanski et al. (2024). We assume an attributability evaluation model
$\mathrm{M_{a}}(E,r)\rightarrow\\{1,0\\}$. The attributability score (AS) of a
response is computed as the proportion of attributable statements.

$\mathrm{ATT}=\frac{1}{n}\sum_{i=1}^{n}\mathrm{M_{a}}(E_{i},r_{i})$

When a response is abstained, we do not evaluate attributability, as no
evidence is required. We additionally evaluate Unanswerable F1 for QA
datasets:

##### Unanswerable F1 (UF1)

We set this up as a classification task similar to Slobodkin et al. (2023).
Gold labels are determined depending on the number of annotations Kwiatkowski
et al. (2019): For 3 annotations or less: “Unanswerable” if all annotators
annotated unanswerable, else “answerable”. More than 3 annotations:
“Unanswerable” if at most one annotator did not annotate unanswerable, else
“answerable”. If a model abstains, its prediction is set to “unanswerable”,
else “answerable”.
To detect abstaining, we compiled a list of keywords based on Slobodkin et al.
(2023) and manual inspection. If a response contained a keyword, any predicted
evidence was removed, and the response was set to "unanswerable" (see §G).

####  3.2.1 Attributability Evaluation Model Selection

To select a model for attributability evaluation, we created test datasets for
QASPER, Natural Questions and GovReport, and evaluated TRUE Honovich et al.
(2022), Attrscore Yue et al. (2023) and Minicheck Tang et al. (2024). For
Attrscore, we map "Contradictory" and "Extrapolatory" predictions to a single
"not attributable" class.

##### Human annotation

We generated attributed responses using GPT 3.5-post-hoc with BM25 for
evidence retrieval. Two authors of this study, both holders of a Master’s
degree and fluent in English, annotated attributability (attributable or not
attributable) for 200 responses (200 sentences for GovReport) and reached an
agreement of 0.74 (QASPER), 0.77 (Natural Questions) and 0.76 (Govreport)
Cohen’s $\kappa$.

##### Results

We report accuracy Gao et al. (2023b) and balanced accuracy Tang et al. (2024)
scores in Table 2. The scores are comparable to Gao et al. (2023b), who
reported 85% accuracy and Tang et al. (2024), who reported between 59% and 84%
balanced accuracy on their respective benchmarks. We select the best-
performing model to evaluate attributability: Minicheck for QASPER, and TRUE
for Natural Questions and GovReport.

Model | QSP | NQ | GR
---|---|---|---
TRUE | 79/80 | 83/83 | 79/78
AttrScore | 76/71 | 68/67 | 76/52
Minicheck | 83/82 | 82/82 | 79/70
Table 2: Attributability evaluation model selection. Metrics: accuracy /
balanced accuracy Tang et al. (2024)

###  3.3 Generation

##### Model selection

We focus on two groups of LLMs with at least 8K tokens input length: (1) The
large state of the art models GPT-3.5888gpt-35-turbo-0613-16k and
GPT-4999gpt-4-turbo-128k, as they hold top positions in other long document
benchmarks Li et al. (2023b); Shaham et al. (2023); An et al. (2023) (2) Small
(~3-7B) models that are accessible with limited resources. We tested a range
of models with citation on QASPER and GovReport and selected Longchat-7B and
Flan-T5-XL as the best performing for prompting and fine-tuning, respectively.
For complete selection results and hyperparameters see §E.

##### Prompts

We employ separate prompt sets for citation and non-citation. Similar to
Shaham et al. (2023), we keep instructions short, including guidance on the
expected responses and output format. Prompts contained three in-context
examples, where documents were shortened to title, section headings and
annotated evidence. For details and prompt optimization experiments, see §C.

###  3.4 Retrieval

##### Retriever selection

For retrieval in post-hoc, retrieve-then-read and reduced, we employ sparse
and dense retrievers that showed good performance in related work101010For
efficiency reasons, we do not use LLMs for retrieval. Thakur et al. (2021):
BM25 Robertson and Zaragoza (2009); Gao et al. (2023b), GTR Ni et al. (2021);
Gao et al. (2023b); Bohnet et al. (2022), Contriever Izacard et al. (2022); Xu
et al. (2024); Bai et al. (2023), Dragon Lin et al. (2023); Xu et al. (2024)
and the best-performing Sentence Transformer “all-mpnet-
base-v2”111111https://sbert.net/docs/sentence_transformer/pretrained_models.html#semantic-
search-models Reimers and Gurevych (2019). For each combination of approach
and task, we selected the best-performing retriever using GPT-3.5 as response
generator (see §F).

##### Details

In retrieve-then-read and reduced, queries were constructed based on
information available in the instruction (e.g. question or a claim). For
GovReport, similar to Zhang et al. (2020), we created queries from all
document segments and retrieved paragraphs based on self-similariy. In post-
hoc, queries were constructed based on the instruction and the generated
response. For both post-hoc and retrieve-then-read, we set $k=5$ for Wice, and
$k=2$ for all other datasets based on evidence statistics (see Table 1). In
reduced approaches we set $k=10$ based on Xu et al. (2024) (see §F).

##  4 Experiments

QSP NQ EI WIC CNLI GR Average EM ATT UF1 EM ATT UF1 CF1 EF1 CF1 EF1 CF1 EF1 RL
ATT RQ EQ GPT-3.5 p-h 45 62 65 51 44 58 77 22 52 48 44 40 26 74 49.17 48.33
rtr 42 78 51 50 42 57 73 25 50 44 47 41 21 40 47.17 45.00 cit 52 60 67 47 38
53 78 50 52 59 43 53 26 59 49.67 53.17 r-p-h 48 71 58 50 45 57 78 22 52 48 45
40 22 64 49.17 48.33 r-cit 50 70 64 46 38 53 78 52 48 63 48 57 22 58 48.67
56.33 GPT-4 p-h 65 68 68 52 42 56 87 24 32 36 66 50 27 63 54.83 47.17 rtr 56
83 56 58 36 61 74 31 22 28 64 59 20 27 49.00 44.00 cit 68 76 71 51 49 57 86 64
35 47 63 64 27 62 55.00 60.33 r-p-h 62 71 69 51 43 57 84 24 28 30 65 50 22 54
52.00 45.33 r-cit 63 76 66 54 49 58 83 49 33 46 64 67 22 56 53.17 57.17
Flan-T5 p-h 55 67 58 81 61 74 86 22 44 46 77 57 27 90 61.67 57.17 rtr 43 74 55
79 62 74 77 25 43 43 64 58 19 67 54.17 54.83 cit 53 57 56 84 75 71 83 59 53 70
77 78 25 71 62.50 68.33 r-p-h 53 72 58 80 62 75 87 22 45 46 76 58 23 84 60.67
57.33 r-cit 52 62 55 84 75 73 84 57 53 68 75 75 22 68 61.67 67.50 Longchat p-h
23 57 62 21 33 38 66 22 23 31 38 33 24 67 32.50 40.50 rtr 29 56 49 27 32 43 43
25 19 25 39 39 21 41 29.67 36.33 cit 22 53 52 17 2 38 66 13 24 19 36 14 23 9
31.33 18.33 r-p-h 33 58 54 25 35 41 61 22 20 27 33 30 22 63 32.33 39.17 r-cit
26 48 53 21 4 42 65 13 20 25 24 12 22 24 29.67 21.00

Table 3: Evaluation on LAB, all scores show percentages. Citation / reduced-
citation mostly perform best, with notable exceptions for GovReport and
Longchat (see §4.1). p-h: post-hoc. rtr: retrieve-then-read. cit: citation.
r-: reduced-. EM: Exact match F1, ATT: Attributability, UF1: Unanswerable F1,
CF1: Classification F1, EF1: Evidence F1, RL: Rouge-L, RQ (EQ): Average
response (evidence) quality, mean of all scores with blue (green) shade.

###  4.1 RQ1: What are optimal approaches to attribution in long document
tasks?

Table 3 shows the results from all combinations of selected models. Due to the
large number of experiments, all results are from single runs.

##### Which approach produces the highest evidence quality?

Flan-T5-XL has higher average scores than GPT-3.5 and GPT-4, while the
Longchat scores are lower. For GPT-3.5, GPT-4 and Flan-T5-XL,
citation/reduced-citation results in the best evidence quality on average and
most datasets, and retrieve-then-read performs worst. Post-hoc works best for
GovReport and Longchat.

##### Does citation hurt response quality?

It could be assumed that post-hoc results in better response quality than
citation, as task decomposition can improve performance Gao et al. (2023a). We
compare average response quality between (reduced-)citation and
(reduced-)post-hoc for GPT-3.5, GPT-4 and Flan-T5-XL. In no case, the response
quality for citation is more than 0.5 points lower than for post-hoc, showing
that citation has a minimal effect on response quality.

##### Does reduction of the input document help?

Comparing reduced-post-hoc to post-hoc and reduced-citation to citation, we
find that response quality is mostly better for the non-reduced variant.
Regarding evidence quality, our findings are not as clear, as reduced-citation
results in the best average evidence quality for GPT-3.5.

##### Discussion

Citation or reduced-citation result in the best average evidence quality,
while not hurting response quality, in line with recent work showing LLM
capabilities for retrieval Ma et al. (2023). The GovReport task and the
Longchat model are exceptions to this, as post-hoc results in better evidence
quality in these cases. For GovReport, the higher evidence quality with post-
hoc can be explained with the "repetitive" nature of the summarization task,
since the high overlap between response statements and document provides good
conditions for retrievers to find evidence. For small models such as Longchat,
related work has shown that they lack instruction following capability to
perform evidence extraction Gao et al. (2023b); Schimanski et al. (2024),
making post-hoc the better approach for the model.
Comparing models, fine-tuned Flan-T5-XL has higher average scores for response
and evidence quality than the large prompted models GPT-3.5 and GPT-4. This
could also be observed in related work Huang et al. (2024); Schimanski et al.
(2024).
Similar to Xu et al. (2024) and Bai et al. (2023), we have not found a general
beneficial effect of context reduction on response quality. The positive
results from Agrawal et al. (2024) were obtained in a multi-document setting,
where there is no logical coherence between the input documents. In contrast,
the logical coherence in long documents can be disrupted through reduction,
which can make processing of the reduced document more difficult.

###  4.2 RQ2: Do LLMs exhibit positional biases in attribution?

Figure 3: Top: Evidence distribution (predicted via citation) by position in
the document. Except for GovReport, no positional bias is visible. Bottom:
Response quality by position of gold evidence in the document. Negative
correlation between evidence position and response quality is visible in
several cases (Table 5) (see §4.2).

Several works have shown that LLM performance depends on the position of
information in the input Liu et al. (2024); Staniszewski et al. (2024); Ravaut
et al. (2024). We investigate whether this phenomenon exists for attribution.

##### Do predicted and gold evidence distributions agree?

Figure 3 (top) shows the predicted evidence distributions from
citation121212We focus on citation because only in this approach the LLM
performs evidence retrieval. and the gold distributions.131313For GovReport,
we matched summary sentences to paragraphs using BM25 to obtain gold evidence,
see §3.1 The models generally follow the gold evidence distribution, with
Longchat showing the strongest deviation. GovReport is an exception: all
models show a higher focus on the beginning of the document, especially
Flan-T5-XL.

##### Does response quality depend on the position of gold evidence?

We grouped instances by evidence position and evaluated the approaches with
full document input (citation and post-hoc) separately for each group (Fig. 3,
bottom). The strong fluctuations of GPT-4 can be explained by the fact that it
was evaluated on 100 samples only. It seems that response quality decreases as
evidence appears later in the document. Similar to Ravaut et al. (2024), we
computed Spearman correlation between response quality and evidence position,
and find that correlation is mostly negative, but not always significant
(Table 5).

##### Discussion

Except for GovReport, we could not find positional biases in LLM evidence
retrieval. From the results of Liu et al. (2024), we expected to find a “Lost
in the Middle” effect, i.e. a reduction of retrieved evidence or performance
in the middle of documents. Rather, we found a decrease in response quality
towards the end of the document similar to Ravaut et al. (2024). We and Ravaut
et al. (2024) work with coherent long documents, while Liu et al. (2024) work
in a RAG-like setup, where the input are multiple documents without coherence,
and models might expect an ordering by relevance. This might explain the
different results.

###  4.3 RQ3: What is the relation between evidence quality and response
quality?

|  | QSP | NQ | EI | WIC | CNLI | GR
---|---|---|---|---|---|---|---
|  | EM | UF1 | EM | UF1 | CF1 | CF1 | CF1 | RL
GPT-3.5 | post-hoc | 3 | 2 | 17 | 20 | 12 | 4 | 19 | 0
citation | 0 | 0 | 14 | 17 | 14 | 4 | 24 | -1
GPT-4 | post-hoc | 4 | 1 | 24 | 26 | 9 | 11 | 16 | 0
citation | 3 | 5 | 9 | 20 | 11 | 3 | 18 | 1
Flan-T5 | post-hoc | -1 | 2 | 14 | 13 | 8 | 0 | 8 | 0
citation | 2 | 1 | 11 | 11 | 11 | 4 | 9 | -1
Longchat | post-hoc | 6 | 2 | 13 | 20 | 18 | 16 | 26 | 0
citation | 1 | 1 | 1 | 2 | 10 | 13 | 15 | 2
Table 4: Difference in response quality-coverage AUC between responses ordered
by evidence quality (attributability) and random ordering. Evidence quality
predicts response quality on several datasets (see §4.3) Figure 4: Gold class
distribution in selective prediction for GPT-3.5-citation.

Attributability evaluation models Honovich et al. (2022); Yue et al. (2023);
Tang et al. (2024) can evaluate evidence quality without external reference.
If evidence quality were positively correlated with response quality, this
could be used to abstain from low-quality responses. We test this with
selective prediction El-Yaniv and Wiener (2010), using attributability scores
as an estimate of confidence.

##### Selective Prediction

We begin by filtering responses that don’t require evidence (e.g.
“unanswerable” on QASPER or “not mentioned” on ContractNLI), as
attributability cannot be evaluated on these. We sort the remaining $l$
predictions by attributability,141414We used MinicheckTang et al. (2024) for
QASPER and TRUE Honovich et al. (2022) for all other datasets (§3.2.1)
obtaining the set of responses $\mathcal{R}_{sel}$ in descending order. For
each $t\in\\{0...l\\}$, we compute average response quality151515As filtering
responses that do not require evidence produces a strong class imbalance, we
use micro F1 instead of macro F1 for Wice and ContractNLI on the subset of
$\mathcal{R}_{sel}$ up to $t$ and the coverage (the proportion of responses
evaluated). We evalate the confidence estimation by the area under the
response quality-coverage curve (AUC) Chen et al. (2023).

##### Is evidence quality a good estimate of confidence in selective
prediction?

Table 4 shows the AUC difference between ordering predictions by
attributability and a random order baseline (mean of 10 repeats). We see that
attributability is an effective estimate of confidence on Natural Questions,
Evidence Inference and ContractNLI, and, to some extent on Wice. On QASPER,
and GovReport, the difference to the random baseline is small.

##### Do unanswerable instances have lower evidence quality?

For instances annotated as “unanswerable”, “not supported” or “not mentioned”,
there is no annotated evidence. If models give different responses, the
evidence quality should be low, and these should be filtered in selective
prediction. Fig. 4 shows the distribution of gold responses in selective
prediction. The proportion of such instances without sufficient evidence
decreases with lower coverage for Natural Questions, Wice and ContractNLI but,
surprisingly, not for QASPER.

##### Why does evidence quality fail to predict response quality?

We consider GovReport a special case, as its long responses are evaluated in
their entirety, which might be too coarse-grained to reflect the per-statement
evaluation of evidence quality. This is corroborated by the fact that system-
level correlation between evidence quality and response quality is
significantly positive for all datasets except GovReport (Table 6).
For Wice and QASPER, the possible causes are: (1) Responses are correct, but
the evidence is insufficient. (2) Responses are incorrect, but the evidence is
sufficient. (3) The attributability scores are wrong. We performed a manual
analysis on the 50 responses with the lowest attributability from
GPT-3.5-citation: For QASPER, the responses were often correct (answer F1 of
66), but the evidence was insufficient in 46 cases. For Wice, this could be
observed in 39 cases. This implies that the LLM’s failure to extract
sufficient evidence is the main reason for low correlation between evidence
and response quality.
For Wice, the attribution evaluation model failed to recognize evidence for
“partially supported” claims in 8 cases, as it is only trained to distinguish
“supported” and “not supported”. This can be seen in Fig. 4, where the
proportion of “partially supported” decreases with lower coverage.

##### Discussion

We explain the differences in the relationship between evidence quality and
response quality by the varying dataset complexity. While Natural Questions,
ContractNLI and Evidence Inference focus on single facts (e.g. a single entity
or a specific contractual obligation), Wice and QASPER instances often contain
multiple facts (e.g. an enumeration or multiple subclaims), which is also
reflected in the number of evidence segments per instance (Table 1). Models
respond correctly, but fail to point to all necessary evidence. This is in
line with related work on attribution: While Bohnet et al. (2022) found that
response quality can be improved by attributability-based reranking on Natural
Questions, Gao et al. (2023b) did not find this on their more complex
benchmark.

##  5 Conclusion

In our experiments on LAB, we found that citation is a promising approach to
attribution for large or fine-tuned models, while for small prompted models,
post-hoc extraction can improve performance. We did not find a “Lost in the
Middle” effect, but negative correlation between evidence position and
response quality in some cases. Finally, we showed that evidence quality can
predict response quality for responses with low complexity. We hope that our
results, code and data spur further research on long document assistants, most
prominently: (1) Improving the citation capabilities of LLMs for complex
responses. (2) Combining attributability evaluation models and iterative self-
refinement Gao et al. (2023a) to try to improve abstained responses.

##  6 Limitations

##### Using LLMs for retrieval

LLMs have shown good performance in reranking tasks (e.g. Ma et al. 2023). For
efficiency reasons, we did not employ LLMs for retrieval. Instead, we employed
state-of-the-art retrievers and implemented a rigorous selection procedure,
elucidating the best retriever for each combination of task and approach. In
the case of reduced approaches, we deem it unlikely to see large beneficial
effects: The “pressure” on the retrievers was already low, as they only had to
retrieve 1-3 relevant segments among 10 retrieved in total.
For post-hoc and retrieve-then-read, it could be that using LLMs for evidence
retrieval improves performance. However this would not change our main claims:
Our experiments with the citation approach already show that using LLMs for
evidence retrieval works best. Therefore, we found the trade-off between
increased computational cost and additional insights not favorable towards
employing LLMs for evidence retrieval in post-hoc and retrieve-then-read. This
might be different for researchers or practicioners interested in maximal
performance.

##### Evaluation of evidence quality

While our attributability model selection experiments showed that these models
obtain good accuracy, our analysis in 4.3 showed that edge cases are not yet
handled well. Research into solving such edge cases is a promising direction
for future work.

##### Datasets

We compiled a benchmark with diverse tasks, domains, response and document
lengths, but naturally, we were not able to cover all variations of these
properties. Many long document datasets and benchmarks are available (e.g. Li
et al. 2023b; An et al. 2023; Shaham et al. 2023), but only few contain
annotated evidence, which we required for the positional bias analysis in our
paper (not finding a long document summarization dataset with annotated
evidence, we resorted to GovReport). Given that we found only limited
positional biases, extending our work to more datasets with and without
annotated evidence is an interesting direction for future work.

##  7 Potential Risks

The goal of this work is to promote the development of more trustworthy long
document assistants, which we deem a promising, but low-risk research goal.
Similarly, we deem our research methodology to be of low risk: All datasets
used were created for NLP research, do not contain personal, harmful or
sensitive data and were published under permissive licenses (Table 7). We did
not employ human annotators other than the authors of the study themselves.

##  8 Acknowledgements

This work was funded by the European Union (ERC, InterText, 101054961). Views
and opinions expressed are however those of the authors only and do not
necessarily reflect those of the European Union or the European Research
Council. Neither the European Union nor the granting authority can be held
responsible for them. This work has been funded by the LOEWE Distinguished
Chair “Ubiquitous Knowledge Processing”, LOEWE initiative, Hesse, Germany
(Grant Number: LOEWE/4a//519/05/00.002(0002)/81). We gratefully acknowledge
the support of Microsoft with a grant for access to OpenAI GPT models via the
Azure cloud (Accelerate Foundation Model Academic Research).

##  9 References

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##  Appendix A Additional Results

###  A.1 Correlation between Evidence Position and Response Quality

See Table 5.

|  | QSP | NQ | EI | WIC | CNLI | GR
---|---|---|---|---|---|---|---
GPT-3.5 | post-hoc | -0.76* | -0.03 | -0.77* | -0.49 | 0.42 | -0.45
citation | -0.26 | -0.44 | -0.62 | -0.41 | -0.02 | -0.59
GPT-4 | post-hoc | 0.12 | 0.02 | 0.03 | 0.35 | 0.04 | 0.12
citation | 0.12 | 0.04 | 0.03 | 0.09 | -0.19 | 0.03
Flan-T5 | post-hoc | -0.32 | 0.05 | -0.75* | -0.37 | -0.07 | -0.92*
citation | -0.19 | -0.13 | -0.81* | -0.76* | -0.26 | -0.95*
Longchat | post-hoc | -0.43 | 0.44 | -0.33 | -0.32 | -0.58 | -0.64*
citation | -0.26 | -0.28 | -0.42 | -0.30 | -0.54 | -0.81*
Table 5: Correlation between response quality and position of annotated
evidence. See §4.2 for details.

###  A.2 System-level Correlation of Attributability and Response Quality

| Pearson $r$ ($p$)
---|---
QSP |  $0.86$ ($6.8\times 10^{-3}$)
NQ |  $0.99$ ($2.7\times 10^{-6}$)
EI |  $0.97$ ($3.9\times 10^{-5}$)
WIC |  $0.90$ ($2.3\times 10^{-3}$)
CNLI |  $0.93$ ($9.4\times 10^{-4}$)
GR |  $0.18$ ($6.7\times 10^{-1}$)
Avg |  $0.98$ ($1.2\times 10^{-5}$)
Table 6: System-level Pearson correlation between response quality and
evidence quality for citation and reduced-citation (8 score pairs per
dataset). For Avg, correlation was computed over the average scores (rightmost
columns in Table 3).

See Table 6

##  Appendix B Datasets

QASPER |  CC-BY 4.0
---|---
Natural Questions |  CC-BY-SA 3.0
Evidence Inference |  MIT
Wice |  CC-BY-SA 4.0 (text), ODC-BY (annotations)
ContractNLI |  CC-BY 4.0
GovReport |  No copyright
Table 7: Licenses of the datasets in LAB.

See Table 8 for examples from the datasets in LAB

##### QASPER

Dasigi et al. (2021) is a dataset of NLP papers and questions about them.
Answers can be extractive, abstractive, “Yes”, “No” or “unanswerable”.
Evidence is annotated on paragraph level. We remove instances with evidence in
tables or figures.

##### Natural Questions

Kwiatkowski et al. (2019) is a dataset of genuine questions from Google search
logs and Wikipedia pages that may or may not contain the answers. We removed
all annotations with answers in tables and those that only have a long answer,
keeping only the annotations that have short answers (i.e. extractive spans),
“Yes”/“No” answers or “unanswerable”. All non-unanswerable annotations have a
single evidence paragraph. As the official test set is hidden, we used the dev
set for testing and a part of the train set for development.

##### Evidence Inference

DeYoung et al. (2020b) consists of reports from clinical studies, "prompts" in
the form of intervention, comparator, and outcome, one or multiple labels for
the prompt ("significantly increased", "significantly decreased", or "no
significant difference") and corresponding evidence spans. We map the
annotated evidence spans to paragraphs.

##### Wice

Kamoi et al. (2023) is a dataset of claims from Wikipedia and referenced
webpages. Claims are annotated as “supported”, “partially supported” or “not
supported”. The referenced webpages are annotated with evidence on sentence
level. We use the full-claim subset.

##### ContractNLI

Koreeda and Manning (2021) is a dataset of non-disclosure agreement contracts
and claims about these agreements. The relation between contract and claim is
annotated as “entailment”, “contradiction” or “not mentioned”. We split the
contract documents into paragraphs at newline symbols to obtain paragraphs,
and map the sentence-level annotated evidence to these paragraphs.

##### GovReport

Huang et al. (2021) is a dataset of reports from US-American governmental
institutions and their executive summaries.

##### Dataset format

All datasets were converted to the Intertext Graph format Kuznetsov et al.
(2022) to enable shared processing and the use of document structure (where
available).

##  Appendix C Prompts

The prompts used in our experiments can be divided into two building blocks:
(1) Instructioncand (2) instance specific input. The instruction further
consists of (a) task explanation and (b) format explanation. We explain the
building blocks in the following. For a complete prompt example, see Table 13.

###  C.1 Instruction

####  C.1.1 Task explanation

Task explanations give information on the type of document used as input, the
type of task to be solved, and possible labels. Table 9 shows the task
explanations used.

####  C.1.2 Format explanation

Format explanations were only used for citation approaches, explaining the
expected format of pointers to evidence segments. Table 10 shows the format
explanations used for single-statement responses and multi-statement
responses. The single statement explanation was used for all datasets except
GovReport, where the multi statement explanation was used.

###  C.2 Instance Specific Input

Instance specific input consisted of an input document and task-dependent
additional information, such as a question or a claim. Table 11 shows the
formatting of instance specific input.

###  C.3 Example document formatting

We shortened the documents in examples to document title, section headings
(where available) and annotated evidence segments. If there were no annotated
evidence segments (e.g. because an example instance is unanswerable) we
selected 2 random segments from the document (5 for Wice).

###  C.4 Prompt Selection

We optimized two prompt properties: The position of the instruction (i.e. task
explanation and format explanation) and the number of few-shot examples. We
ran experiments employing GPT-3.5 on QASPER and GovReport under the citation
approach, the results are shown in Table 12. We first varied the position of
the instruction, finding that the instruction before the instance specific
input resulted in best performance. Next, we experimented with using 1, 2 or 3
few shot examples, finding that 3 examples resulted in best performance. We
limited the number of few-shot examples to leave enough space for the input
document.

###  C.5 Complete prompt example

See Table 13.

##  Appendix D Attributability Evaluation

To evaluate attributability, we experimented with TRUE Honovich et al. (2022),
Attrscore (Flan-T5-XXL version) Yue et al. (2023) and Minicheck (Flan-T5-Large
version) Tang et al. (2024). These models expect a claim and evidence as
input. In the following we explain the construction of claims, evidence
formatting and model-specific prompts.

###  D.1 Claim Construction

Claims were constructed based on the task specific inputs and outputs. Table
14 shows examples.

##### QA datasets

For QASPER and Natural Questions, question and answer were concatenated to get
the claim.

##### Evidence Inference

If the response was “no significant difference”, the claim was formulated as
“There was no significant difference between the effect of {intervention} and
the effect of {comparator} on {outcome}.”. If the response was “significantly
increased” or “significantly decreased” were predicted, the claim was
formulated as “The {intervention} {response} {outcome} in comparison to
{comparator}”.

##### Wice

If the response was “supported”, the input claim was used as the claim for
attributability evaluation. If the response was “partially supported”, the
claim for attributability evaluation was formulated as “The claim {claim} is
partially supported.” “Not supported” responses did not require
attributability evaluation.

##### ContractNLI

If the response was “entailment”, the input claim was used as the claim for
attributability evaluation. As there are only 20 claims in the complete
dataset, we formulated an inverse version of each claim. This was used as the
claim when the response was “contradiction”. See Table 14 for an example.

##### GovReport

The generated summary sentences were used as claims.

###  D.2 Input Formatting

##### Evidence formatting

Predicted evidence segments were ordered by occurence in the document, joined
by newline symbols, and prepended with the document title.

##### Model-specific formatting

The prompt templates for attributability evaluation can be seen in Table 15.
They were taken from the respective original publications.

###  D.3 Annotation Instructions

As mentioned in 3.2.1, we manually annotated predictions on QASPER, Natural
Questions and GovReport. The annotation instructions are shown in Fig. 5.

##  Appendix E Generation

###  E.1 Model Selection

To select open source models for prompting and fine-tuning, we compared their
performance in preliminary experiments. Table 16 shows all open source models
considered.

##### Fine-tuning

We fine-tuned all candidate models for 1000 steps on QASPER and evaluated them
on the dev set. As the results in Table 17 show that Flan-T5-XL has a clear
advantage over the other models, we used it in all further fine-tuning
experiments.

##### Prompting

We evaluated all candidate models on 100 instances from the QASPER and
GovReport dev sets, using the citation approach with the prompts described in
§C. Table 18 shows the results. As the Longchat model obtained the highest
average score, we used it in all further experiments.

###  E.2 Hyperparameters

##### Generation

We set the maximum input length to 16K, truncating the input document if
needed. We performed greedy decoding and temperature 0 for best
reproducibility (§G).

##### Fine-tuning

We employed LoRA fine-tuning Hu et al. (2022) in a citation setting and a non-
citation setting for 1000 steps. We set $r=64$, $\alpha=16$, a dropout rate of
$0.1$, a learning rate of $10^{-}4$, effective batch size of $8$, and employed
an AdamW optimizer Loshchilov and Hutter (2019)

##  Appendix F Retrieval

###  F.1 Retriever Selection

##### Candidates

We experimented with BM25 Robertson and Zaragoza (2009), GTR Ni et al. (2021),
Contriever Izacard et al. (2022), Dragon Lin et al. (2023) and the best-
performing Sentence Transformer “all-mpnet-
base-v2”161616https://sbert.net/docs/sentence_transformer/pretrained_models.html#semantic-
search-models Reimers and Gurevych (2019).

##### Results

We tested all combinations of post-hoc (Table 21), retrieve-then-read (Table
19) and reduced-citation (Table 20) approaches, tasks and retrievers, using
GPT-3.5 to generate responses. We selected the best-performing retriever for
each combination

###  F.2 Query Construction

#####  Post-hoc query construction

Post-hoc queries were constructed by combining instance specific inputs and
outputs. The exact query construction depended on the task. For QASPER and
Natural Questions, question and generated response were concatenated. For
Evidence Inference, ContractNLI and GovReport, post-hoc queries were
constructed in the same manner as claims for attributability evaluation (see
§D). For Wice, the input claim was used as the query. See Table 22 for
examples.

#####  Reduce and retrieve-then-read query construction

Queries for Reduce and retrieve-then-read were constructed based on instance
specific input. For QASPER and Natural Questions, this was the question. For
Evidence Inference, this was the question formed out of intervention,
comparator and outcome. For Wice and ContractNLI, this was the claim. For
GovReport, these were the document paragraphs. See Table 23 for examples.

#####  Reduce and retrieve-then-read for GovReport

To find the most relevant paragraphs from documents in the GovReport dataset,
we used each paragraph as a query and computed the retrieval score for all
paragraphs (including the paragraph itself), resulting in $n^{2}$ scores
$s_{i,j}$ for a document with $n$ paragraphs. Each $s_{i,j}$ is the score for
retrieving $p_{j}$ with query $p_{i}$. We compute a single score for each
paragraph as $s_{j}^{*}=\sum_{i=0}^{n}s_{i,j}$, i.e. the sum of scores to
retrieve $p_{j}$. The paragraphs with the highest $s^{*}$ are then selected.

##  Appendix G Technical Details

##### Technical setup

GPT-35 and GPT-4 were accessed via the Azure OpenAI API
171717https://azure.microsoft.com/en-us/products/ai-services/openai-service,
all other models were downloaded and run locally via Huggingface Transformers
Wolf et al. (2020) on NVIDIA A100 and H100 GPUs.

##### Rouge Scoring

We used the rouge-score package181818https://pypi.org/project/rouge-score/ to
evaluate ROUGE-L

##### Compute Budget

We spent around $400 to access OpenAI models, including preliminary
experiments. We estimate to have spent 300 GPU hours on all experiments,
including fine tuning, inference and attributability evaluation.

##### Use of AI assistants

We used Github Copilot191919https://github.com/features/copilot for coding
assistance.

|  Input |  Output
---|---|---
QASPER |  Question: Which domains do they explore? |  news articles related to Islam and articles discussing Islam basics
Natural Questions |  Question: who won the 2017 ncaa mens basketball tournament |  North Carolina
Evidence Inference |  Question: With respect to Quality of life, characterize the reported difference between patients receiving good motivation/capability and those receiving inadequate motivation/capability.Choose between ’significantly decreased’, ’no significant difference’, and ’significantly increased’. |  no significant difference
Wice |  Claim: Having over 3,000 animals of nearly 400 different species, the zoo has slowly increased its visitors and now ranks as the number one outdoor tourist attraction in the state.
Additional Info: The Sedgwick County Zoo is an AZA-accredited wildlife park and major attraction in Wichita, Kansas. Founded in 1971 with the help of the Sedgwick County Zoological Society, the zoo has quickly become recognized both nationally and internationally for its support of conservation programs and successful breeding of rare and endangered species. |  Partially Supported
ContractNLI |  Claim: Receiving Party shall not reverse engineer any objects which embody Disclosing Party’s Confidential Information. |  not mentioned
GovReport |  Question: Write a one-page summary of the document. Structure your summary according to the following questions: 1. Why GAO Did This Study 2. What GAO Found 3. What GAO Recommends |  {summary}
Table 8: Dataset examples QASPER |  You are given a Scientific Article and a Question. Answer the Question as concisely as you can, using a single phrase or sentence. If the Question cannot be answered based on the information in the Article, answer "unanswerable". If the question is a yes/no question, your answer should be "yes", "no", or "unanswerable". Do not provide any explanation. (If the question can be answered, provide one or several evidence paragraphs that can be used to verify the answer. Give as few paragraphs as possible.)
---|---
Natural Questions |  You are given a Wikipedia page and a question about that page. Answer the question as concisely as you can, using at most five (5) words. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation. (If the question can be answered, provide one evidence paragraph that can be used to verify the answer.)
Evidence Inference |  You are given a clinical study report and a question. Assess the effect of a treatment on a clinical outcome, compared to a control treatment. The options are "significantly increased", "significantly decreased" and "no significant difference". Do not provide any explanation. (Provide one or several evidence paragraphs that can be used to verify the answer. Give as few paragraphs as possible.)
Wice |  You are given a document and a claim. Evaluate if the claim is supported by the document. You can choose between “supported”, “partially supported” and “not supported”. Do not add any explanation. (If you answer “supported” or “partially supported”, provide the evidence sentences from the document that can be used to verify the answer. Give as few sentences as possible.)
ContractNLI |  You are given a non disclosure agreement contract and a statement. Determine the relation between the contract and the statement. You can choose between “entailment”, “contradiction” and “not mentioned”. Do not add any explanation. (If you answer “entailment” or “contradiction”, provide the evidence paragraphs from the contract that can be used to verify the answer. Give as few paragraphs as possible.)
GovReport |  You are given a government report document. Write a one page summary of the document. (Each sentence in your summary should reference the source paragraphs from the document that can be used to verify the summary sentence.)
Table 9: Task explanations for the datasets in LAB. Text in parentheses at the end was only shown when citation approaches were used. Single statement |  Your reply must have the following format:
"<answer> [X] [Y]"
In your reply, replace <answer> with your solution to the task. Your reply
must be followed by the ids of the relevant segments from the document.
---|---
Multi statement |  Your reply must have the following format:
"<answer_sentence_1>[X] [Y] <answer_sentence_2>[Z]…"
In your reply, replace <answer_sentence_1> with your first sentence,
<answer_sentence_2> with your second sentence, and so forth. Each sentence
must be followed by the ids of the segments relevant to the sentence.
Table 10: Format explanations. Multi statement was used for GovReport, Single Statement for all other datasets. QSP |  Scientific Article: {document} [End of Document]
Question: {question}
---|---
NQ |  Document: {document} [End of Document]
Question: {question}
EI |  Document: {document} [End of Document]
Question: {question}
WIC |  Document: {document} [End of Document]
Claim: {statement}
Additional Info: {additional_info}
CNLI |  Contract: {document} [End of Document]
Statement: {statement}
GR |  Document: {document} [End of Document]
{question}
Table 11: Formatting of instance specific input. See Table 8 for examples of task specific inputs. | QSP | GR
---|---|---
| AF1 | ATT | UF1 | Avg | RL | ATT | Avg
Inst before and after | 42 | 75 | 14 | 43.77 | 22 | 43 | 32.60
Inst before | 47 | 70 | 33 | 50.31 | 25 | 10 | 17.62
Inst after | 40 | 60 | 20 | 40.16 | 21 | 14 | 17.85
Inst before, 1 example | 53 | 67 | 43 | 54.21 | 26 | 36 | 30.97
Inst before, 2 examples | 50 | 59 | 42 | 50.40 | 26 | 44 | 35.11
Inst before, 3 examples | 50 | 66 | 67 | 60.86 | 28 | 54 | 41.08
Table 12: Prompt optimization results on GPT-3.5 under the citation approach. Inst before / after refers to the position of the instruction being before / after the task specific input. For complete instructions, see C Avg: Average of scores for one task. Task Explanation |  You are given a Scientific Article and a Question. Answer the Question as concisely as you can, using a single phrase or sentence. If the Question cannot be answered based on the information in the Article, answer "unanswerable". If the question is a yes/no question, your answer should be "yes", "no", or "unanswerable". Do not provide any explanation. (If the question can be answered, provide one or several evidence paragraphs that can be used to verify the answer. Give as few paragraphs as possible.)
---|---
Format explanation |  Your reply must have the following format:
"<answer> [X] [Y]"
In your reply, replace <answer> with your solution to the task. Your reply
must be followed by the ids of the relevant segments from the document.
Example 1 |  Scientific Article: Automated Hate Speech Detection and the Problem of Offensive Language
Abstract {omitted} [End of Document]
Question: What type of model do they train?
Example 2 |  {omitted}
Example 3 |  {omitted}
Instance specific input |  Scientific Article: Combining Thesaurus Knowledge and Probabilistic Topic Models
Abstract {omitted} [End of Document]
Question: Which domains do they explore?
Table 13: Example of the final prompt format used for citation on QASPER. | Input |  Claim
---|---|---
QASPER | Question, response |  “The answer to the question ’Which domains do they explore?’ is ’news articles related to Islam and articles discussing Islam basics”’
Natural Questions | Question, response |  “The answer to the question ’who won the 2017 ncaa mens basketball tournament?’ is ’North Carolina”’
"Evidence Inference | Question, response |  “There was no significant difference between the effect of good motivation/capability and the effect of inadequate motivation/capability on quality of life.”
“Good motivation/capability [significantly increased/significantly decreased]
quality of life compared to inadequate motivation/capability”’
"Wice | Claim, response |  “Having over 3,000 animals of nearly 400 different species, the zoo has slowly increased its visitors and now ranks as the number one outdoor tourist attraction in the state.”
“The claim ’Having over 3,000 animals of nearly 400 different species, the zoo
has slowly increased its visitors and now ranks as the number one outdoor
tourist attraction in the state’ is partially supported.”
"ContractNLI | Claim, response |  “Receiving Party shall not reverse engineer any objects which embody Disclosing Party’s Confidential Information.”
“Receiving Party may reverse engineer any objects which embody Disclosing
Party’s Confidential Information.”’
GovReport | Response statement |  “Improper payments in Medicaid increased from $29.1 billion in fiscal year 2015 to $36.7 billion in fiscal year 2017.”
Table 14: Examples for claims constructed for attributability evaluation. TRUE |  premise: {evidence} hypothesis: {claim}
---|---
Attrscore |  Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Verify whether a given reference can support the claim. Options:
""Attributable, Extrapolatory or Contradictory.
### Input:
Claim: {claim}
Reference: {evidence}
### Response:
Minicheck |  predict: {evidence}</s>{claim}
Table 15: Prompts for attributability evaluation models based on the respective publications. | #Params |  Reference
---|---|---
Gemma-7b-it | 7B |  Team et al. (2024)
GritLM-7B | 7B |  Muennighoff et al. (2024)
Mistral-7B-Instruct-v0.2 | 7B |  Jiang et al. (2023)
Vicuna-7B-v1.5-16K | 7B |  Chiang et al. (2023)
Flan-T5-XL/XXL* | 3B/11B |  Longpre et al. (2023)
LongChat-7B-v1.5-32K | 7B |  Li et al. (2023a)
Llama3-8B-Instruct | 8B |  See 202020https://llama.meta.com/llama3/
Table 16: Open source models considered in selection experiments. *: Flan-T5-XL was used in fine-tuning, Flan-T5-XXL was used in prompting experiments. | AF1 | ATT | UF1 | Avg
---|---|---|---|---
Gemma | 0.41 | 0.28 | 0.25 | 0.27
GritLM | 0.42 | 0.30 | 0.22 | 0.26
Mistral | 0.46 | 0.34 | 0.26 | 0.30
Vicuna | 0.42 | 0.31 | 0.24 | 0.28
Flan-T5-XL | 0.45 | 0.61 | 0.22 | 0.42
LongChat | 0.42 | 0.29 | 0.24 | 0.26
Llama3 | 0.44 | 0.37 | 0.25 | 0.31
Table 17: results of Fine-tuning Model Selection | QASPER | GovReport | Avg
---|---|---|---
Model | Answer F1 | Attr | Unans F1 | R-L | Attr |
Gemma | 22 | 22 | 0 | 20 | 1 | 12.51
GritLM | 26 | 46 | 0 | 22 | 0 | 17.44
Mistral | 31 | 47 | 0 | 0.25 | 0 | 19.33
Vicuna-7B | 21 | 42 | 0 | 23 | 2 | 16.67
Flan-T5-XXL | 26 | 28 | 36 | 12 | 1 | 18.32
LongChat | 21 | 52 | 0 | 0.25 | 16 | 22.31
Llama3 | 17 | 60 | 0 | 23 | 0 | 18.78
Table 18: results of OS Prompt Model Selection | QASPER | Natural Questions
---|---|---
| AF1 | ATT | UF1 | Avg | AF1 | ATT | UF1 | Avg
BM25 | 32 | 60 | 42 | 44.49 | 44 | 33 | 59 | 45.17
SBERT | 29 | 69 | 39 | 45.56 | 47 | 53 | 60 | 53.36
Contriever | 37 | 75 | 50 | 54.20 | 47 | 51 | 61 | 52.89
Dragon | 39 | 79 | 52 | 56.65 | 48 | 48 | 63 | 52.93
GTR | 36 | 70 | 46 | 50.54 | 46 | 48 | 61 | 51.47
| Evidence Inference | Wice
| CF1 | EF1 | Avg | - | CF1 | EF1 | Avg | -
BM25 | 77 | 16 | 46.46 | - | 29 | 36 | 32.71 | -
SBERT | 78 | 23 | 50.51 | - | 33 | 43 | 42.50 | -
Contriever | 83 | 29 | 55.71 | - | 26 | 42 | 33.87 | -
Dragon | 78 | 33 | 55.23 | - | 31 | 41 | 36.29 | -
GTR | 78 | 33 | 55.58 | - | 33 | 43 | 38.20 | -
| Contract NLI | Govreport
| CF1 | EF1 | Avg | - | RL | ATT | Avg |
BM25 | 43 | 34 | 38.35 | - | 27 | 33 | 29.96 | -
SBERT | 42 | 35 | 38.21 | - | 28 | 36 | 31.79 | -
Contriever | 38 | 37 | 36.80 | - | 24 | 30 | 26.92 | -
Dragon | 39 | 37 | 37.64 | - | 21 | 19 | 20.16 | -
GTR | 44 | 39 | 41.39 | - | 23 | 37 | 30.00 | -
Table 19: Retriever selection for retrieve-then-read. Retrievers were combined with GPT-3.5 and were evaluated on 100 dev instances per dataset. The retriever that resulted in the best average score was used in all further experiments for the respective combination of retrieve-then-read and task. | QASPER | Natural Questions
---|---|---
| AF1 | ATT | UF1 | Avg | AF1 | ATT | UF1 | Avg
BM25 | 45 | 67 | 63 | 58.33 | 42 | 36 | 59 | 45.69
SBERT | 40 | 65 | 51 | 51.79 | 41 | 39 | 55 | 45.13
Contriever | 48 | 72 | 65 | 61.64 | 40 | 42 | 55 | 45.83
Dragon | 49 | 73 | 56 | 59.20 | 42 | 42 | 58 | 46.95
GTR | 47 | 71 | 63 | 60.17 | 42 | 41 | 58 | 46.63
| Evidence Inference | Wice
| CF1 | EF1 | Avg | - | CF1 | EF1 | Avg | -
BM25 | 83 | 49 | 66.12 | - | 44 | 61 | 52.38 | -
SBERT | 82 | 50 | 65.75 | - | 40 | 65 | 52.13 | -
Contriever | 83 | 57 | 70.42 | - | 36 | 60 | 48.14 | -
Dragon | 87 | 54 | 70.51 | - | 37 | 64 | 50.49 | -
GTR | 86 | 62 | 73.66 | - | 37 | 62 | 49.75 | -
| Contract NLI | Govreport
| CF1 | EF1 | Avg | - | RL | ATT | Avg |
BM25 | 42 | 49 | 45.25 | - | 26 | 51 | 38.29 | -
SBERT | 44 | 56 | 50.11 | - | 27 | 60 | 43.38 | -
Contriever | 45 | 58 | 51.42 | - | 23 | 45 | 33.93 | -
Dragon | 52 | 55 | 53.29 | - | 22 | 43 | 32.12 | -
GTR | 46 | 54 | 49.94 | - | 23 | 51 | 37.23 | -
Table 20: Retriever selection for reduced approaches. Retrievers were combined with GPT-3.5-reduced-citation and were evaluated on 100 dev instances per dataset. The retriever the resulted in the best average score was used in all further experiments for the respective combination of reduced-citation / reduced-post-hoc and task. | QASPER | Natural Questions
---|---|---
| AF1 | ATT | UF1 | Avg | AF1 | ATT | UF1 | Avg
BM25 | 52 | 65 | 73 | 63.25 | 41 | 40 | 57 | 45.92
SBERT | 52 | 55 | 73 | 64.12 | 41 | 50 | 57 | 53.35
Contriever | 52 | 63 | 73 | 68.42 | 41 | 54 | 57 | 55.28
Dragon | 52 | 69 | 73 | 71.11 | 41 | 54 | 57 | 55.28
GTR | 52 | 59 | 73 | 66.27 | 41 | 54 | 57 | 55.28
| Evidence Inference | Wice
| CF1 | EF1 | Avg | - | CF1 | EF1 | Avg | -
BM25 | 86 | 25 | 55.60 | - | 86 | 25 | 55.60 | -
SBERT | 86 | 20 | 52.94 | - | 86 | 20 | 52.94 | -
Contriever | 86 | 27 | 56.60 | - | 86 | 27 | 56.60 | -
Dragon | 86 | 28 | 57.27 | - | 86 | 28 | 57.27 | -
GTR | 86 | 24 | 55.27 | - | 86 | 24 | 55.27 | -
| Contract NLI | Govreport
| CF1 | EF1 | Avg | - | RL | ATT | Avg |
BM25 | 46 | 36 | 41.14 | - | 28 | 73 | 50.55 | -
SBERT | 46 | 36 | 41.24 | - | 28 | 60 | 44.14 | -
Contriever | 46 | 39 | 42.69 | - | 28 | 65 | 46.63 | -
Dragon | 46 | 40 | 43.16 | - | 28 | 68 | 48.22 | -
GTR | 46 | 41 | 43.57 | - | 28 | 61 | 44.61 | -
Table 21: Retriever selection for post-hoc approaches. Retrievers were combined with GPT-3.5-post-hoc and were evaluated on 100 dev instances per dataset. The retriever the resulted in the best average score was used in all further experiments for the respective combination of post-hoc and task. | Input |  Query
---|---|---
QASPER | Question, response |  “Which domains do they explore? news articles related to Islam and articles discussing Islam basics”
Natural Questions | Question, response |  “who won the 2017 ncaa mens basketball tournament? North Carolina”
Evidence Inference | Question, response |  “There was no significant difference between the effect of good motivation/capability and the effect of inadequate motivation/capability on quality of life.”
“Good motivation/capability [significantly increased/significantly decreased]
quality of life compared to inadequate motivation/capability”
Wice | Claim |  “Having over 3,000 animals of nearly 400 different species, the zoo has slowly increased its visitors and now ranks as the number one outdoor tourist attraction in the state”
ContractNLI | Claim, response |  “Receiving Party shall not reverse engineer any objects which embody Disclosing Party’s Confidential Information.”
“Receiving Party may reverse engineer any objects which embody Disclosing
Party’s Confidential Information.”
GovReport | Response statement |  “Improper payments in Medicaid increased from $29.1 billion in fiscal year 2015 to $36.7 billion in fiscal year 2017.”
Table 22: Examples for queries for post-hoc evidence retrieval. | Input |  Query
---|---|---
QASPER | Question |  “Which domains do they explore?”
Natural Questions | Question |  “who won the 2017 ncaa mens basketball tournament?”
Evidence Inference | Question |  “With respect to Quality of life, characterize the reported difference between patients receiving good motivation/capability and those receiving inadequate motivation/capability. Choose between ’significantly decreased’, ’no significant difference’, and ’significantly increased’.”
Wice | Claim |  “Having over 3,000 animals of nearly 400 different species, the zoo has slowly increased its visitors and now ranks as the number one outdoor tourist attraction in the state”
ContractNLI | Claim |  “Receiving Party shall not reverse engineer any objects which embody Disclosing Party’s Confidential Information.”
GovReport | Paragraph |  “Medicaid has been on our high-risk list since 2003, in part, because of concerns about the adequacy of fiscal oversight and the program’s improper payments—including payments made…”
Table 23: Examples for queries for retrieve-then-read and reduced retrieval.
Figure 5: Annotation instructions for attributability model evaluation.