#  ExpertQA : Expert-Curated Questions and Attributed Answers

Chaitanya Malaviya, Subin Lee, Sihao Chen, Elizabeth Sieber,
Mark Yatskar, Dan Roth
University of Pennsylvania University of Washington
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>
###### Abstract

As language models are adapted by a more sophisticated and diverse set of
users, the importance of guaranteeing that they provide factually correct
information supported by verifiable sources is critical across fields of study
& professions. This is especially the case for high-stakes fields, such as
medicine and law, where the risk of propagating false information is high and
can lead to undesirable societal consequences. Previous work studying
factuality and attribution has not focused on analyzing these characteristics
of language model outputs in domain-specific scenarios. In this work, we
present an evaluation study analyzing various axes of factuality and
attribution provided in responses from a few systems, by bringing domain
experts in the loop. Specifically, we first collect expert-curated questions
from 484 participants across 32 fields of study, and then ask the same experts
to evaluate generated responses to their own questions. We also ask experts to
revise answers produced by language models, which leads to ExpertQA, a high-
quality long-form QA dataset with 2177 questions spanning 32 fields, along
with verified answers and attributions for claims in the answers.111Code and
dataset is available at https://github.com/chaitanyamalaviya/expertqa.

##  1 Introduction

Figure 1: ExpertQA contains 2177 information-seeking questions formulated by
experts spanning 32 fields, as well as expert-verified, model-generated
answers to these questions. Each claim-evidence pair in an answer is judged by
experts for various properties such as the claim’s informativeness,
factuality, citeworthiness, whether the claim is supported by the evidence,
and reliability of the evidence source. Further, experts revise the original
claims to ensure they are factual and supported by trustworthy sources.

As the influence of large language models (LLMs) grows beyond the computer
science community, _experts_ from various fields are rapidly adapting LLMs for
assistance in information-seeking scenarios. For example, medical
professionals are using these systems for performing differential diagnosis
Lee et al. (2023) and researchers are using them for faster literature surveys
Krenn et al. (2022); Birhane et al. (2023); Owens (2023). While the use of
LLMs in specialized domains has many potential benefits, it also carries
significant risks. False or hallucinated claims that are confidently phrased
can potentially mislead experts and propagate societal harms, especially in
high stakes domains such as medicine or law Evans et al. (2021); Dash et al.
(2023); Volokh (2023). Such claims can cause frustration and distrust in AI
tools among individual experts in the mildest case, and propagation of
misinformation and unsafe practices, in the worst case.

Providing citations or attributions within generated responses is a promising
direction for alleviating such concerns. However, the quality of these
attributions in model-generated responses, as well as the factuality of
responses, is understudied in domain-specific settings. This is partly because
we do not completely understand the specific information-seeking needs of
experts. Although experts from different fields are naturally best suited to
aid with such an evaluation, expert evaluations are rarely conducted, as
bringing experts in the loop can be time-consuming and costly.

To bridge this gap, we construct ExpertQA, a benchmark of information-seeking
questions curated by experts from 32 diverse fields. ExpertQA includes
questions relevant to each field, as well as judgements from experts about how
well several state-of-the-art systems perform along various axes of factuality
and attribution. Having experts in the loop allows us to model a realistic
information-seeking scenario that helps us understand how people in different
fields use LLMs and where their capabilities fall short.

ExpertQA is constructed by asking qualified experts to formulate questions
from their field that they are curious about or have encountered in their
professional lives (§2.1). Responses to these questions are collected from a
set of LLM-based systems that produce attributions for their answers (§3).
These include purely generative, retrieval-augmented, and post-hoc attribution
systems. We then ask experts to validate the claims and evidences found in the
responses to their own questions (§2.2). Experts judge each claim for its
informativeness to the question, its citeworthiness, and factuality. They are
also asked to judge how faithful the claim is to an accompanying evidence and
rate the reliability of the evidence source. Finally, experts revise each
claim so it is faithful to a reliable source and make a best effort attempt at
ensuring the claim is factual. This overall process is described in Figure 1.

We first use ExpertQA to evaluate representative systems from which responses
are sampled (§4). Our findings suggest that:

  1. 1.

Retrieve-and-read systems often generate complete attributions compared to LLM
prompting and post-hoc attribution, but struggle to produce citations for all
cite-worthy claims.

  2. 2.

The retrieval source significantly impacts the quality of attribution and
overall factuality.

  3. 3.

High-stakes domains such as medicine and law suffer from a large percentage of
incomplete attributions (35% and 31% incomplete attributions respectively) and
many attributions come from unreliable sources (51% attributions are rated as
not reliable by experts).

We also measure the extent to which existing automatic methods for attribution
and factuality estimation Bohnet et al. (2022); Min et al. (2023) correlate
with expert judgements (§5). We find that these metrics fall short in
correlating with reference judgements of attribution and factuality. However,
adapting these metrics to our data through finetuning results in improvements
across domains.

The revised answers we collect can be used for improving and evaluating future
models on long-form question answering. While similar datasets have been
proposed Fan et al. (2019), examples in ExpertQA are more realistic and
contain verified answers edited by experts. Furthermore, unlike existing
datasets, questions in ExpertQA contain few vague questions because they
include questions professionals have encountered in their practice. We
establish several baselines and show that we can improve models by finetuning
on ExpertQA but that there is substantial room for improvement, both in terms
of ROUGE and QAFactEval (§6).

##  2 ExpertQA: Annotation Tasks

Field |  Question | Types
---|---|---
Anthropology |  Why is it that Africa’s representation is still a problem in modern day times regardless of the academic writings that state otherwise? | II,VII
Architecture |  Suppose an architect decides to reuse an existing foundation of a demolished building, what is to be considered to ensure success of the project? | IV
Biology |  Can you explain the mechanisms by which habitat fragmentation affects biodiversity and ecosystem functioning, and provide examples of effective strategies for mitigating these impacts? | III,VI
Chemistry |  Why does gallic acid have an affinity with trivalent iron ions? | I
Engineering & Technology |  How different will licensing a small modular reactor be as compared to licensing traditional large nuclear power plants? | VII
Healthcare/Medicine |  If a 48 year old woman is found to have an esophageal carcinoma that invades the muscularis propria and has regional lymph node metastases but no distant metastasis, what is her stage of cancer and what are possible recommended treatments? | I,III
Law |  Can direct evidence in a case that has been obtained illegally be considered by the court in some cases if it directly points to the defendant’s guilt? | I
Music |  What exercises would you do in a singing class with a teenager with puberphonia? | IV
Physics & Astronomy |  Standard Model does not contain enough CP violating phenomena in order to explain baryon asymmetry. Suppose the existence of such phenomena. Can you propose a way to experimentally observe them? | V
Political Science |  Despite the fact that IPCC was formed in 1988, several studies have showed that argubaly more than 50% of all carbon emissions in history have been released since 1988. What does this show about IPCC and developed countries’ efforts? | VII
Visual Arts |  Tell me the step by step process of recycling a canvas. | III

Table 1: Examples from ExpertQA. See Table 14 for a larger list showing an
example from all fields.

|  Question Type | Count
---|---|---
I |  Directed question that has a single unambiguous answer | 444
II |  Open-ended question that is potentially ambiguous | 528
III |  Summarization of information on a topic | 371
IV |  Advice or suggestions on how to approach a problem | 251
V |  Question that describes a hypothetical scenario and asks a question based on this scenario | 853
VI |  Request for a list of resources where one can find more information | 160
VII |  Request for opinion on a topic | 207

Table 2: Question types categorized according to various information needs
that are part of ExpertQA.

The annotation is conducted in multiple longitudinal stages and we describe
each of these below. In the first stage, we ask experts to write questions
from their field (§2.1). In the next stage, we present responses sampled from
various systems back to the same experts for analysis (§2.2). Further details
about annotator backgrounds, annotation cost and screenshots of our interface,
are presented in Appendix A.

###  2.1 Stage 1: Expert-Curated Questions

Participants are recruited through Prolific and are qualified as experts if
they have attained a formal education in the field and have worked in the
field for at least 3 years. They are first asked to write questions from their
field of expertise. They are told that this question could be one they have
encountered in their profession or one they are curious about. We ask them to
formulate challenging technical questions, for which it may not be possible to
find a single document on the web that answers them completely.

Each expert is asked to write 5 questions and to specify the question type(s)
for each question (as shown in Table 2). We formulate these question types
broadly based on existing work that attempts to classify information needs
Rose and Levinson (2004). Because of their practical nature, at least two of
the questions are required to be scenario-based questions (Type V, Table 2).
We collect more than 3000 questions this way from 524 experts across 32
fields. We manually filter all these questions for coherence and relevance to
the field and our initial question corpus contains 2507 questions. Examples of
these questions from different fields are presented in Table 1.

###  2.2 Stage 2: Answer and Claim Annotation

Next, we generate responses for the questions from stage 1 by prompting six
different systems that provide attributions with their answers. These systems
are described in §3. We split each answer into claims, where claims are
considered at the granularity of a sentence and extracted using the spaCy
sentence tokenizer Honnibal and Montani (2017).222We also considered further
increasing the atomicity of claims (like Kamoi et al. (2023)) but finer-
grained atomic claims incur considerably higher annotation cost.

Figure 2: The distribution of questions across different fields in ExpertQA.

In this stage of annotation, experts validate responses to their own
questions. This is beneficial as experts are best qualified to evaluate
answers to their own questions. We noticed a low attrition rate in our study
as around 92% of annotators from stage 1 validated at least 1 of their own
questions in stage 2. Since this task is intensive, a single annotation task
is broken down into 1-3 question-answer pairs. The following properties of
answers and claims are evaluated, and are presented to annotators in the same
order as below. Properties that judge answer quality are marked with  and
those that judge evidence quality are marked with .

#### () Answer Usefulness.

Participants are first asked to judge whether the complete answer is useful in
answering the question. Usefulness is measured based on whether the answer is
at least partially responding to the question. Usefulness is marked on a scale
of {useful, partially useful, not useful at all}.

#### ( \+ ) Claim / Evidence Attribution.

Attribution is judged based on whether a claim is supported by its
accompanying evidence, following a similar design as Rashkin et al. (2021);
Bohnet et al. (2022). Support may be judged as complete, partial or
incomplete. If no evidence is provided, support needs to be marked Missing and
if the evidence is inaccessible, it needs to be marked N/A. Annotators are
told that they can assume that certain common sense facts don’t need to be
explicitly stated in the evidence to judge support. If the evidence included
multiple documents, annotators judge support for the claim collectively using
all documents. Taking inspiration from Kamoi et al. (2023), if the claim is
partially supported, annotators are asked to specify the unsupported span and
the reason why it is unsupported.

Evidences can come in the form of URLs or passage evidences, depending on the
system. Judging attribution can be difficult for passage evidences as relevant
context might be missing, so we provide URLs along with attributed passages
for context. Annotators are instructed to only use the passage for judging
attribution in these cases.

#### () Claim Informativeness.

To differentiate between the relevance of different claims for the question,
we asked annotators to label informativeness of each claim. A claim may be
judged as central to answering the question (very relevant), making a relevant
point that is slightly important to answer the question (a bit relevant),
making a relevant point that isn’t too relevant to answering the question (not
too important) or making a peripheral point that is not relevant to answering
the question (uninformative).

#### () Claim Factuality.

Next, we ask annotators to label their best estimate of the factual
correctness of each claim. They are asked to judge factuality based on their
own expertise, the evidence returned by the system, and minimal browsing on
the internet if needed (lasting no longer than 2-3 minutes). This judgement is
also collected on a Likert scale (Definitely correct, Probably correct,
Unsure, Likely incorrect and Definitely incorrect). We ask annotators to be
conservative in their judgements of factual correctness, labeling Definitely
correct only if every word in the claim is correct.

#### () Reliability of Source Evidence.

Experts may judge certain sources as more reliable than others. For example,
webmd.com is a patient-facing website for medical information that may be
considered reliable by non-medical experts, but medical experts may not put as
much trust into the evidence from this website. Catering attributions to
domain experts will require presenting attributions that they consider as
credible. Therefore, we ask annotators if the accompanying evidence (if any)
is found on a website they would consider reliable (on a scale of Reliable,
Somewhat Reliable, Not reliable at all).

Figure 3: The classes of systems from which we sampled responses (answers +
attributions) for evaluation.

#### () Worthiness of Claim Citation.

Previous work has highlighted that not all claims are equally worthy of
citation Bohnet et al. (2022); Alam et al. (2023). Certain claims might be
considered too obvious or fundamental to a user’s domain of expertise and
hence, might not need a citation (for example, the definition of a cell for a
biologist). Annotators are asked to simply state if the claim is necessary to
be cited (as a binary choice). We note that the cite-worthiness of a given
claim might depend heavily on a user’s prior knowledge and needs, which can
vary across experts from the same field.

#### ( \+ ) Claim/Evidence Revision.

After labeling the above properties, annotators edit the claim and evidences
to ensure that the claim is factually correct and the given references support
the claim. For instance, if the claim is false or uninformative, annotators
could delete it. Further, if the evidence is incorrect or insufficient, they
could remove it. While doing so, we did not require them to replace
missing/incorrect evidence with correct evidences because doing that can be
too time-consuming.

##  3 Systems Evaluated

We now describe the classes of systems from which we sampled responses for
evaluation (also see Figure 3). All systems produce an answer string and
attributions in the form of in-line citations for the sentences in the answer.
Attributions are either returned as URLs to webpages or passages along with
URLs to webpages from where they are retrieved. Further experimental details
and prompts are included in Appendix B.

#### LLM as generator + retriever.

In this paradigm, we prompt large language models in a closed-book fashion
Brown et al. (2020); OpenAI (2023) to generate an answer with in-line
citations where they provide URLs for each citation. Note that this is
unlikely to work as the model essentially has to generate a URL from its
parametric memory. Nevertheless, we consider GPT-4 as the LLM from which we
sample responses (gpt4). The prompt used is given in Table 9.

#### Post-hoc retrieval.

This system differs from the above, as we only prompt LLMs to generate answers
without attribution, and perform retrieval of evidence for a claim as a post-
hoc step. This renders the attributions naturally unfaithful, but we believe
this is still a worthwhile approach to investigate because of the strength of
LLMs as generators and retrievers independently. The attribution corpora we
consider are Sphere Piktus et al. (2021) (post_hoc_sphere_gpt4), which is a
large static dump of CommonCrawl, and Google search results
(post_hoc_gs_gpt4).

#### Retrieve-and-read.

In this class of systems, we first retrieve evidence for a question and then
generate an answer by prompting a model to use the retrieved evidence to
answer the question. As our attribution corpus, we again consider Sphere
Piktus et al. (2021) (rr_sphere_gpt4) as well as Google search results
(rr_gs_gpt4). We use sparse retrieval using BM25 Robertson et al. (2009) for
retrieving from Sphere. We then generate an answer using GPT-4, by including
the retrieved evidence as context in our prompt. The answer generator is
instructed to also generate in-line citations for each sentence, which refer
to the passages provided in the context. The prompt used is given in Table 10.

#### Commercial systems.

We also consider commercial systems that have recently gained popularity, such
as BingChat. The precise implementation of these systems is proprietary, but
we can still draw conclusions about the utility of these systems for domain
experts through our human evaluation. In this work, we sample responses using
the balanced mode of BingChat (bing_chat).

System | Count | Abstention Rate
---|---|---
gpt4 | 174 | 0%
bing_chat | 470 | 0.01%
rr_sphere_gpt4 | 279 | 37.89%
rr_gs_gpt4 | 452 | 22.69%
post_hoc_sphere_gpt4 | 403 | 0%
post_hoc_gs_gpt4 | 399 | 0%

Table 3: Number of examples sampled from different systems and the abstention
rates of different systems.

###  3.1 Response Sampling

We sample uniformly from all systems but exclude all abstained answers and
constrain the number of claims for each answer to be at most 10 for lowering
annotation costs. During annotation, we noticed that attributions from gpt4
often pointed to broken links and evaluating such attributions is not
meaningful, so we sampled fewer responses from that system. The number of
examples included from each system and the abstention rate of the systems (how
frequently a system responds with one of the predefined strings indicating it
cannot provide an answer) is presented in Table 3.

##  4 Analysis

###  4.1 Data Statistics

Figure 4: Histogram of the number of claims and number of tokens across all
examples in ExpertQA.

The total number of examples validated in stage 2 of our annotation is 2177.
The average number of claims and average number of tokens across these
examples is 5.79 and 152.12 respectively (the overall distributions are
presented in Figure 4). The distribution of examples across fields is
presented in Figure 2. A large percentage of examples in ExpertQA come from
high-stakes fields such as Healthcare/Medicine and Law. Table 2 presents the
distribution of questions across different question types shown to annotators
in stage 1. The largest number of questions are Type V because participants
were asked to write 2/5 questions of this type. After those, the largest
number of questions are open-ended questions (Type I) and directed questions
(Type II).

###  4.2 Manual Analysis

To estimate the agreement of human labels in ExpertQA, we (the authors)
labeled our agreement with the reference labels from two fields to which the
authors collectively belong. Specifically, we sampled 60 questions from
Engineering and Technology and another 60 from Healthcare / Medicine, where we
included 10 questions from each system. For each claim in the responses to
these 60 questions, we label binary agreement with the annotated properties
from §2.2.

Figure 5: Percentage agreement on claim annotations for a random sample of 60
questions each from the Engineering & Technology and Medicine fields.

Our analysis showed fairly high agreement (> 85%) for most labels in both
fields. The agreement for attribution labels for medical claims was found to
be slightly lower than engineering claims. We noticed that medical claims
allow for more nuance in interpretation, compared to more objective claims in
engineering.

Figure 6: The Likert distribution of labels for the different properties of
answers / claims, annotated by experts. The top 3 properties (answer
usefulness, claim informativeness and factuality) are judgements of answer
quality and the bottom 3 (claim/evidence attribution, source reliability and
claim cite-worthiness) are attribution quality.

###  4.3 Results

We present the Likert distribution for claims across all systems and
properties in Figure 6. Below we summarize our main conclusions from the
analysis.

#### Majority of answers are useful, but answers from purely generative
systems are considered more useful.

We find that $\sim$87-89% of answers from gpt4 are marked useful. The
retrieve-and-read systems (as well as bing_chat, which also retrieves
evidences first) are marked slightly less useful (73-80%), likely because
retrieved evidences are not always highly relevant. Choosing relevant
evidences using Google search results in more useful answers than with the
smaller Sphere corpus and a sparse retriever.

#### Retrieve-and-read systems often generate complete attributions, but
struggle to produce citations for all cite-worthy claims.

Retrieve-and-read systems have a stronger inductive bias to use the retrieved
evidence to answer the question. However, they do not always produce
attributions for cite-worthy claims (18% of these claims are missing
attributions)333Figure 6 shows the Likert distribution of attribution labels
on those claims deemed cite-worthy by experts.. On the other hand, post-hoc
attribution systems return attributions for every single claim, by definition,
but return more incomplete attributions. Lack of context during post-hoc
retrieval can be an issue for retrieving valid attributions. For example, a
claim such as "Targeted therapies, such as PARP inhibitors or CDK4/6
inhibitors, may be useful depending on the specific genetic makeup or
molecular features of the tumor [2]." does not actually contain any details
about the fact that the question asked about breast cancer, which can make it
hard to find relevant retrievals. Therefore, we might need approaches that can
make claims standalone, such as Choi et al. (2021), to improve post-hoc
citation quality.

Finally, without retrieval, we found that gpt4 often generates citations to
URLs that link to plausible & trustworthy domains (for eg, nasa.gov for
astronomy and nih.gov for medical claims), but the content on these webpages
is often totally mismatched (more than 60% of the time).

#### Both vanilla prompting and retrieval-augmented systems generate mostly
very relevant claims to the question.

At the same time, a significant percentage of claims (30-40) are not very
relevant. This may include void claims (that simply restate the question or
state simplistic facts). This suggests that there is a lot of room in making
answers concise and relevant. In addition, using Sphere as the retrieval
corpus results in fewer informative claims than Google search for retrieve-
and-read systems.

#### Just over half the claims are labeled as definitely correct by experts.

While a significant percentage of claims are labeled as correct (probably or
definitely), experts do not instill high confidence in the factual correctness
of claims. This might be because it is hard to judge factuality with a high
degree of confidence, in a short time frame. Once again, a smaller retrieval
corpus and retriever (rr_sphere_gpt4) results in less factual claims as the
model may be more likely to hallucinate.

#### The retrieval corpus has a significant effect on expert judgements of
source reliability.

Expert judgements of reliability are directly influenced by the credibility of
the sources from which evidences are retrieved. Corpora such as Sphere, which
do not account for source, often present evidences that are unreliable to
experts (for both rr_sphere_gpt4 and post_hoc_sphere_gpt4). For example, in a
question about breast cancer, evidence from a comment on a blog is retrieved
and is naturally considered Not reliable at all by the expert. Using Google
search as the retrieval system, improves source reliability judged by experts.

#### Majority of claims are deemed cite-worthy across systems.

Only around 17-22% claims are judged not citeworthy by the experts. This
suggests that most claims in responses to expert-curated warrant providing
supporting evidence. Non-cite-worthy claims are mostly either uninformative or
too basic for an expert from the field.

#### Effect of Retrieval Corpus.

While sampling responses, we instruct models to abstain if they cannot answer
the question faithfully and accurately. Among retrieve-and-read systems, we
find that the abstention rate is significantly higher for systems that use
Sphere as the retrieval corpus compared to Google search. Across claim
properties, we find that the retrieval corpus has a significant impact on
human judgements. Systems that use Sphere as the retrieval corpus instead of
Google search result in more missing attributions, fewer correct and
informative claims, attributions with more evidences from unreliable sources,
and overall less useful answers.

#### Domain and Question Type Trends.

Figure 10 shows the distribution of labels across all fields. There are few
clearly discernible patterns from the trends across fields. The percentage of
claims labeled as being Definitely or Probably correct is fairly high (>85%)
for many fields. However, we note that across all annotated claims, high-
stakes domains such as medicine and law can suffer from a significant
percentage of incomplete attributions (around 35% of medical claims and 31% of
legal claims worthy of citation are not supported with appropriate evidence).
Further, a large percentage of claims present evidences from unreliable
sources for these domains (for eg, $\sim$51% of medical claims have
attributions from sources that are not labeled Reliable by experts).

Across question types, systems clearly struggle with Type VI questions that
request for a list of resources, as claims are less informative, factual, and
supported by evidence. Other question types that appear to be challenging are
Type IV and Type VII, which seek advice for a problem or opinions on a topic
respectively.

##  5 Automatic Estimation of Attribution and Factuality

Next, we study the effectiveness of current automatic systems for attribution
Honovich et al. (2022) and factuality (correctness) estimation Min et al.
(2023) in the context of ExpertQA. In both cases, we observe that current
systems show high precision but low recall when compared with human judgements
of attribution and factuality.

###  5.1 Automatic Attribution Estimation

Under the framework of _attributable to identified sources_ (AIS) Rashkin et
al. (2021), i.e. judging whether model generated content can be verified
against given attributions, previous work has found NLI models to be effective
in providing automated AIS (AutoAIS) estimations (Gao et al., 2023; Bohnet et
al., 2022).

System | AutoAIS | Num. Claims
---|---|---
gpt4 | .156 | 149
bing_chat | .320 | 992
rr_sphere_gpt4 | .689 | 732
rr_gs_gpt4 | .778 | 1415
post_hoc_sphere_gpt4 | .281 | 1158
post_hoc_gs_gpt4 | .241 | 1500

Table 4: AutoAIS score (more attributable$\rightarrow$1, less
attributable$\rightarrow$0) of predicted responses by the systems. Only claims
annotated as _citeworthy_ and _with complete support_ are considered.

To understand the effectiveness of AutoAIS on ExpertQA, we follow the settings
in previous work and use the NLI
classifier444https://huggingface.co/google/t5_xxl_true_nli_mixture from
Honovich et al. (2022) as an AutoAIS system to predict the attribution labels
of claim-evidence pairs in ExpertQA. The model gives a binary decision for
whether a hypothesis claim is entailed by the evidence as premise. For
evidence longer than the maximum sequence length of the model (512), we apply
the retrieve-and-rerank technique from Schuster et al. (2022), where we split
evidence into sentences and take top-2 sentences with highest entailment
confidence predicted by the NLI model as evidence.

Table 4 shows the macro-averaged AutoAIS scores for the set of claims marked
as having attribution with complete support from each system. Compared to our
human judgments, the AutoAIS estimations show a large variance in terms of the
averaged completeness of attribution across systems. Notably, attributions
generated by post-hoc retrieval receive much lower AutoAIS scores, whereas
retrieve-and-read systems get higher scores.

System | zero-shot | finetuned
---|---|---
P | R | F1 | P | R | F1
gpt4 | .33 | .02 | .05 | .52 | .32 | .39
bing_chat | .97 | .26 | .41 | .90 | .90 | .90
rr_sphere_gpt4 | .89 | .59 | .71 | .83 | .90 | .87
rr_gs_gpt4 | .86 | .74 | .79 | .87 | .98 | .92
post_hoc_sphere_gpt4 | .92 | .28 | .43 | .79 | .97 | .87
post_hoc_gs_gpt4 | .87 | .17 | .29 | .77 | .95 | .85
all | .88 | .38 | .53 | .82 | .91 | .86

Table 5: Precision, Recall and F1 scores of AutoAIS labels predicted by the
TRUE NLI model (0-shot vs. finetuned version on the ExpertQA train split)
against human attribution judgements in ExpertQA. Error Type: Fine-grained
Information Sensitivity.
---
Claim (post_hoc_sphere_gpt4): For water with a low pH (acidic), you can add a
base or alkaline compound, such as baking soda (sodium bicarbonate) or calcium
carbonate, to raise the pH [1].
Attribution [1]: … To raise or lower pH, a pool custodian simply adds acids or
alkalis into the water. For example, adding sodium carbonate (soda ash) or
sodium bicarbonate (baking soda) will generally raise the pH and adding
muriatic acid or sodium bisulfate will lower the pH.
Human: Cite-Worthy & Complete Support
AutoAIS: 0 (No or Partial Support).
Error Type: Multi-Source Attributions.
Claim (bing_chat): Other radiological signs of fetal death include gas in the
fetus or in the portal and umbilical vessels [1], and Deuel’s halo sign [2].
Attribution [1]: … Intrafetal gas is an unequivocal sign of fetal death
provided it can be conclusively differentiated from maternal gas, shadows. …
Attribution [2]: Radiological investigation is warranted in the antenatal
patient only if the findings are likely to influence future management. The
major radiological signs of fetal death include overlapping of the cranial
bones and Deuel’s halo sign
Human: Cite-Worthy & Complete Support
AutoAIS: 0 (No or Partial Support).
Table 6: Examples of typical errors of AutoAIS against human judgements in
ExpertQA.

We compare the per-claim AutoAIS predictions to human judgement of attribution
completeness in Table 5. The results suggest that AutoAIS estimations with a
NLI model overall have high-precision yet low-recall against human judgements
of attribution completeness. To understand the discrepancy between NLI model
behavior vs. human judgement of attribution completeness, we highlight a few
typical examples of attribution(s) in Table 6. For NLI, every part of the
hypothesis or claim is expected to be directly verifiable against the premise,
while we observe during attribution verification, human judgements involve
more implicit world knowledge, e.g. _calcium carbonate is an alkali_. Another
common type of mistake from AutoAIS involves combining information from
multiple pieces of evidence. We observe mutli-source attributions to be
particularly common among bing_chat and retrieve-and-read systems.

###  5.2 Automatic Factuality Estimation

System | F1 (T) | F1 (F) | F1 (overall)
---|---|---|---
gpt4 | 0.919 | 0.108 | 0.852
bing_chat | 0.912 | 0.134 | 0.841
rr_sphere_gpt4 | 0.884 | 0.106 | 0.795
rr_gs_gpt4 | 0.927 | 0.068 | 0.865
post_hoc_sphere_gpt4 | 0.898 | 0.132 | 0.817
post_hoc_gs_gpt4 | 0.939 | 0.158 | 0.886
all | 0.915 | 0.119 | 0.844

Table 7: FActscore F1 scores on reference factuality labels for claims in
ExpertQA.

Prior work has proposed methods Manakul et al. (2023); Min et al. (2023) to
automatically estimate the factuality of model-generated responses. In
particular, we use and study FActScore Min et al. (2023) as an automatic
estimator for the factuality of claims. We first break down each claim into
finer-grained atomic claims using few-shot prompting with text-davinci-003,
using the same prompt and setting as Min et al. (2023). We retrieve the top-k
(k=3) relevant passages using Google search using an atomic claim as the
query. The evidence paragraphs for each atomic claim are then included in a
prompt sent to gpt-3.5-turbo, along with the atomic claim, where the model is
instructed to say whether the atomic claim is True or False. The final
factuality score for a claim is then calculated by averaging the scores of all
the atomic claims.

In Table 7, we report the F1 scores of the factual (T) and non-factual (F)
classes and the micro-averaged overall F1 scores of the FActScore factuality
scores and the reference factuality labels. FActscore scores are thresholded
at 0.5 to get binary scores and reference factuality labels are 1 if the
claim’s factuality is labeled as Probably correct or Definitely correct, and 0
otherwise.

We find that automatic factuality estimation struggles to identify non-factual
claims in our dataset. In particular, predicted labels have low recall of non-
factual claims. This is more often the case for retrieve-and-read systems,
where the answer is generated based on retrieved evidences. The other systems
use GPT4’s parametric knowledge for answer generation, which might make it
slightly easier for an evaluator like ChatGPT to judge factuality.

##  6 Long-form QA Evaluation

A beneficial output of our annotation pipeline is the revised answers produced
by annotators. These answers are vetted by experts to contain factual
information and compose a new long-form QA dataset, ExpertQA. We consider two
types of splits for ExpertQA (both 80-10-10): a random split of the data and a
domain-wise split, where 80% of a field’s data is included in the training set
and 10% is included in both validation and test sets.

###  6.1 Evaluation Metrics

We consider standard metrics used for evaluating long-form question answering,
that are based on similarity to a reference answer, i.e., ROUGE Lin (2004) and
those focused on evaluating factual consistency through question-answer pairs
generated on a reference answer, i.e., QAFactEval Fabbri et al. (2022).

###  6.2 Baselines

We finetune the following open-source language models: FlanT5-11B Chung et al.
(2022), Alpaca-7B Taori et al. (2023), Vicuna-7B Chiang et al. (2023) and
LLaMa2-7B-Chat Touvron et al. (2023). We finetune these models with the same
prompts as the ones used in their training (provided in Tables 11, 12).
Further, we also report results with Llama2-70B-Chat without finetuning
(marked *).

Split | Model | R1 | R2 | RL | QFE
---|---|---|---|---|---
Random | FlanT5-11B | 0.335 | 0.114 | 0.215 | 2.068
Vicuna-7B | 0.351 | 0.119 | 0.212 | 1.068
Llama2-7B | 0.362 | 0.125 | 0.219 | 1.985
Llama2-70B* | 0.320 | 0.101 | 0.181 | 1.050
Domain | FlanT5-11B | 0.324 | 0.107 | 0.210 | 1.538
Vicuna-7B | 0.359 | 0.120 | 0.213 | 1.739
Llama2-7B | 0.363 | 0.124 | 0.219 | 1.726
Llama2-70B* | 0.328 | 0.104 | 0.187 | 0.979

Table 8: Long-form QA results after finetuning models on the random and domain
splits of ExpertQA.

###  6.3 Results

Our results are shown in Table 8. We find that both Llama2-7B and Vicuna-7B
outperform FlanT5-11B despite the smaller model size, likely due to additional
instruction finetuning for both those models. We observe that finetuning
significantly improves performance (results without finetuning are in Table
13), and Llama2-70B performs worse than finetuned systems under zero-shot
prompting.

##  7 Related Work

#### Attribution-Generating Methods.

With the rapid adaptation of large language models, a few different classes of
systems have been proposed for generating attributions for the responses they
produce. The first class of systems is vanilla LLM prompting Tay et al.
(2022); Weller et al. (2023), where LLMs such as GPT-4 OpenAI (2023) are
prompted to return attributions (in the form of titles of references,
optionally accompanied with URLs) along with their answers. Since these
systems are not explicitly trained to produce attributions, they can often
hallucinate the references they provide Agrawal et al. (2023). Another class
of systems is retrieve-and-read systems Guu et al. (2020); Borgeaud et al.
(2022); Izacard et al. (2022), which first retrieve evidence relevant for a
query, and then generate an answer based on the retrieved evidence. These
systems are sometimes trained on demonstrations of humans browsing for
information Nakano et al. (2021); Thoppilan et al. (2022); Menick et al.
(2022), which allows them to jointly generate answers and citations. Finally,
post-hoc retrieval Gao et al. (2023); He et al. (2022) involves retrieving
attributions after answering a query using both the query and response for
retrieval, and optionally revising the answer based on the attribution. For
sampling responses, we consider all three classes of systems described above.

#### Attribution Analysis

Prior work has conducted analysis of attributions produced by systems in
response to queries from existing NLP datasets Rashkin et al. (2021); Bohnet
et al. (2022); Dziri et al. (2022); Liu et al. (2023); Muller et al. (2023).
Notably, Rashkin et al. (2021) propose the framework of Attributable to
Identified Sources (AIS) for performing human evaluation of attributions.
Analysis from these works suggests that systems are still far from providing
precise attributions with sufficient recall for all citeworthy statements. In
our work, we recognize that this is problematic in specific domains where
attribution precision and recall are both critical.

Automatic methods to measure attribution have also been explored. Briefly,
attribution has been automatically measured through the use of textual
entailment models Bohnet et al. (2022); Yue et al. (2023), by checking the
consistency of answers produced to the same question using the evidence or the
response Wang et al. (2020) and prompting LLMs and finetuning LLMs on tasks
such as NLI relevant for judging attributions Yue et al. (2023).

Previous efforts at collecting gold attribution data have been conducted by
repurposing Wikipedia citations Kamoi et al. (2023); Petroni et al. (2022), or
existing datasets like Natural Questions Bohnet et al. (2022); Kwiatkowski et
al. (2019), and relying on large-scale human annotation Chen et al. (2022);
Dziri et al. (2022); Kamalloo et al. (2023). We note that with regards to the
information needs of experts, this data can be restricting in terms of the
accompanying corpus, and contain limited complexity when it comes to expert-
curated content (for instance, experts are likely to use and trust sources
other than Wikipedia).

#### Factuality Analysis.

Analysis of factuality or truthfulness of LM generations has been conducted
extensively in prior work Thorne et al. (2018); Evans et al. (2021); Maynez et
al. (2020); Pagnoni et al. (2021); Lin et al. (2021); Muhlgay et al. (2023).
Factuality is closely linked to work studying hallucinations Ji et al. (2023),
which includes closed-domain hallucination Kryscinski et al. (2020); Maynez et
al. (2020), where hallucinations are analyzed in reference to some context, as
well as open-domain hallucination (for example, Manakul et al. (2023)). The
factuality labels we collect as part of ExpertQA elicit a best-effort
judgement of truthfulness of claims from experts.

Prior methods proposed to verify the factuality of statements include zero-
resource approaches Manakul et al. (2023); Kadavath et al. (2022); Agrawal et
al. (2023); Azaria and Mitchell (2023); Min et al. (2023), which operate
mostly in a black-box fashion, and resource-enriched approaches Thorne et al.
(2018); Guo et al. (2022); Chen et al. (2023); Feng et al. (2023), that verify
statements by checking their validity against external databases or resources.
We use one such recent method called FactScore Min et al. (2023) to evaluate
how well the factuality labels in our dataset correlate with automatic
factuality judgements. Min et al. (2023) split claims into finer-grained
atomic claims and using LMs to verify these atomic claims. Dedicated
benchmarks to study hallucination have also been proposed Liu et al. (2022);
Li et al. (2023), but these are often synthetically generated and not based on
real user queries and responses to them.

#### Long-form QA.

Existing long-form QA datasets are often created using naturally occurring
sources on the web such as search queries Nguyen et al. (2016); Stelmakh et
al. (2022) and forums Fan et al. (2019). Several issues have been pointed out
with conducting robust evaluation for long-form QA Krishna et al. (2021).
Previous work has also suggested that evaluating finer-grained claims provides
higher agreement of factuality among annotators Krishna et al. (2023). Keeping
this in mind, we construct ExpertQA to cover practical information needs of
experts and collect fine-grained judgements of factuality along with vetted
answers.

Xu et al. (2023) also conduct human evaluation of long-form answers with
experts from 7 fields. Notably, they emphasize the importance of evaluating
multiple aspects of long-form answers, which are also considered in our work.

#### Domain-specific QA.

Several domain-specific datasets for question answering have also been
proposed in prior work. Specifically, existing work has presented datasets for
the medical domain Tsatsaronis et al. (2015); Pampari et al. (2018); Jin et
al. (2019, 2021); Pal et al. (2022), legal Guha et al. (2023), technology Dos
Santos et al. (2015) and others. There is also work that proposes datasets
with examples spanning multiple domains Rogers et al. (2020); Reddy et al.
(2019); Hendrycks et al. (2021). However, these datasets often have a very
limited coverage of domains. Importantly, most of these are not created with
experts-in-the-loop and hence may not always represent natural information
seeking distributions, that go beyond factoid questions. Further, they do not
contain long-form answers and attributions for the answers. Finally, because
of large-scale pretraining, we are aware that many such datasets are under the
risk of contamination Sainz et al. (2023). Hence, we choose to crowdsource
questions from experts, and further also get annotations for attribution and
factuality.

##  8 Conclusion and Future Work

Our study suggests that although large language models show a lot of promise
for aiding domain experts, there is large ground to cover in being able to
provide expert-level guidance that is factual and also supported by reliable
sources Metzler et al. (2021). While LLMs make information access and search
substantially easier for domain experts, improving factuality and attribution
of answers is necessary to improve trustworthiness in these systems.
Repurposing general-purpose language models for domain-specific purposes
requires having experts-in-the-loop, so we can understand their information
needs and how models fall short in meeting them. We hope that our benchmark,
ExpertQA, can benefit the community with improved methods for attribution &
factuality estimation, and long-form question answering evaluation.

##  9 Limitations

#### Atomicity of Claims.

In most cases, claims in our dataset are sentences that may not represent
singular information units. This lack of atomicity in claims means that
properties such as factuality and attribution need to be judged exhaustively
for a claim. Collecting human judgements for finer-grained atomic claims can
be significantly more expensive and is not explored in this work.

#### Claim Extraction.

Extracting sentence-level claims from a generated answer for the purpose of
evaluation is performed by using a sentence tokenizer. However, we note that
existing tokenizers suffer from sentence tokenization errors (for example,
when lists or tables are present in answers). This resulted in a small number
of claims being excessively long and hard to evaluate.

#### Field Coverage.

Even though we tried to cover a wide range of fields in our dataset, we missed
covering questions from certain fields. Finding experts from rarer fields can
be especially hard. We will consider further expanding ExpertQA to more
domains, so that it can be more broadly useful. In addition, the examples in
our dataset represent the information needs of English-speaking annotators
primarily based in Europe, the Americas and Africa.

#### Subjectivity of labels.

Some of the properties of claims can elicit more subjective judgements, which
can vary between experts from the same field. This subjectivity is not
inherently captured in our data through multiple judgements, but we do
estimate agreement using claims from engineering and medicine through our own
labels (§4.2).

## Acknowledgements

First, we would like to thank the 484 annotators who took the time and effort
out to help out with this study. We would also like to thank Artemis
Panagopoulou, Alyssa Hwang, Nelson Liu and Chris Alberti for helpful comments
and discussions.

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##  Appendix A Annotation Details

#### Annotator backgrounds.

The 484 participants involved in our study came from 26 different countries,
across Europe, Africa, Oceania, North and South America. The participants were
recruited through Prolific, a crowdsourcing
platform555https://www.prolific.co. To qualify as experts, participants were
required to have attained a formal education in the field and have worked in
the area for at least 3 years. Participants were told that their annotations
will be used to evaluate the capabilities of large language models to provide
truthful answers with well-supported evidences to questions from different
fields. They were also informed that the data will be released publicly upon
the completion of the study.

#### Annotator fields.

The initial set of fields were listed by going through university department
names, and ensuring that we cover a wide range of disciplines. Upon completing
stage 1 of our annotation, we further refined these fields to represent a
diverse set, for which we have enough experts.

#### Annotation costs.

In both stage 1 and stage 2, annotators were compensated at the rate of $15
per hour with additional bonuses when annotators spent more time than we
anticipated. The average time taken for stage 2 annotations was 13.83 minutes
per question-answer pair, and there was a lot of variance in time taken across
annotators.

#### Annotation Interface.

Figure 7 and Figure 8 show screenshots of our stage 2 annotation interface in
the order the task was presented to annotators.

Figure 7: Screenshots of the interface (1-4).

Figure 8: Screenshots of the interface (5-9).

##  Appendix B Experimental Details

###  B.1 Hyperparameter Settings

#### Response collection.

Across all systems, for generating responses from gpt4, we use a temperature
of 1.0, and a maximum length of 2048 tokens. For all retrieval componenets, we
use text-embedding-ada-002 as the embedding model. The retrieve-and-read
systems first retrieve top-k (k=5) evidence passages from Sphere or top-10
Google search results using the question as the retrieval query. Google search
results are split into passages of 1000 tokens with 200 tokens of overlap
between subsequent chunks.

On the other hand, the post-hoc citation systems simply use the claims from
gpt4 responses, but generate their own attributions by retrieving evidence for
each claim in the answer. Post-hoc retrieval systems use the top-k passages
(k=5) retrieved from Sphere or the top-10 Google search results with the claim
as the retrieval query. Search result are split into passages the same way as
retrieve-and-read systems.

#### Automatic attribution and factuality estimation.

For automatic attribution with AutoAIS, we use the
t5_xxl_true_nli_mixture666https://huggingface.co/google/t5_xxl_true_nli_mixture
with 11B parameters by Honovich et al. (2022). For finetuning the
t5_xxl_true_nli_mixture model on the train split of ExpertQA, we use the
DeepSpeed ZeRO optimization Rajbhandari et al. (2020) with stage 3, a batch
size of 1, a learning rate of $1e^{-4}$ and train models for 3 epochs.

#### Long-form QA.

For finetuning FlanT5-11B, we use a batch size of 2, maximum sequence length
of 512, a learning rate of 1e-4 and train models for 3 epochs. For finetuning
both Llama2-7B and Vicuna-7B, we use a batch size of 4, maximum sequence
length of 2048, learning rate of $2e^{-4}$ and train models for 3 epochs.

###  B.2 Prompts

The prompts used to generate responses from gpt4 and bing_chat is provided in
Table 9, while the prompt used to generate responses for retrieve-and-read
systems is in Table 10.

For factuality estimation, we use the same prompts as Min et al. (2023) for
both claim decomposition and atomic claim factuality prediction. Finally, for
long-form QA baselines, we use the prompt in Table 11 for Llama and Table 12
for Vicuna.

Vanilla LM QA Prompt
---
Answer the question completely and precisely in up to 500 words. You must
provide in-line citations to each statement in the answer. The citations
should appear as numbers such as [1], [2] and contain references to valid URLs
on the web. A statement may need to be supported by multiple references and
should then be cited as [1] [2]. Question: I work in the field of [FIELD]. My
question is: [QUESTION] Answer:
Table 9: QA Prompt for GPT4 and BingChat. Retrieve-and-read Prompt
---
Use the following pieces of context to answer the question completely and
precisely in up to 500 words. If you don’t know the answer, just say "I don’t
know" and explain why the context is insufficient to answer the question. You
need to support every statement in the answer with in-line citations to
passages given in the the context. The citations should appear as numbers such
as [1], [2] that refer to the Passage IDs of the given passages. A statement
may need to be supported by multiple references and should then be cited as
[1] [2]. (for example, "Paris is the capital of France [1] [2]." where "1" and
"2" are the Passage IDs of the first and second passage).  [CONTEXT] Question:
[QUESTION] Answer:
Table 10: Retrieve-and-read QA prompt. Llama2 Prompt
---
<s>[INST] <<SYS>> You are a helpful, respectful and honest assistant. Always
answer as helpfully as possible, while being safe. Your answers should not
include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal
content. Please ensure that your responses are socially unbiased and positive
in nature. If a question does not make any sense, or is not factually
coherent, explain why instead of answering something not correct. If you don’t
know the answer to a question, please don’t share false information. <</SYS>>
[QUESTION] [/INST]
Table 11: Llama2 prompt for long-form QA. Vicuna Prompt
---
A chat between a curious user and an artificial intelligence assistant. The
assistant gives helpful, detailed, and polite answers to the user’s questions.
USER: [QUESTION]ASSISTANT:
Table 12: Vicuna prompt for long-form QA.

##  Appendix C Additional Plots

Split | Model | R1 | R2 | RL | QFE
---|---|---|---|---|---
| FlanT5-11B* | 0.074 | 0.023 | 0.063 | 0.000
| FlanT5-11B | 0.335 | 0.114 | 0.215 | 2.068
| Vicuna-7B* | 0.358 | 0.116 | 0.209 | 0.902
Random | Vicuna-7B | 0.351 | 0.119 | 0.212 | 1.068
Llama2-7B* | 0.300 | 0.083 | 0.167 | 1.359
Llama2-7B | 0.362 | 0.125 | 0.219 | 1.985
Llama2-70B* | 0.320 | 0.101 | 0.181 | 1.050
| FlanT5-11B* | 0.073 | 0.023 | 0.062 | 0.000
| FlanT5-11B | 0.324 | 0.107 | 0.210 | 1.538
| Vicuna-7B* | 0.352 | 0.114 | 0.203 | 2.596
Domain | Vicuna-7B | 0.359 | 0.120 | 0.213 | 1.739
Llama2-7B* | 0.303 | 0.087 | 0.169 | 1.799
Llama2-7B | 0.363 | 0.124 | 0.219 | 1.726
Llama2-70B* | 0.328 | 0.104 | 0.187 | 0.979
Table 13: Long-form QA results before (marked with *) and after finetuning
models on the random and domain splits of ExpertQA.

Examples from all fields included in ExpertQA are shown in Table 14. We show
the distribution of all question types (from Table 2) across all fields that
are part of ExpertQA in Figure 9.

In Table 10, we summarize the label distribution of all claim properties
across fields and in Table 11, we summarize the label distribution of all
claim properties across question types.

In Table 13, we summarize results on long-form QA before and after finetuning
models on both ExpertQA splits.

Field |  Question | Types
---|---|---
Anthropology |  Why is it that Africa’s representation is still a problem in modern day times regardless of the academic writings that state otherwise? | II,VII
Architecture |  Suppose an architect decides to reuse an existing foundation of a demolished building, what is to be considered to ensure success of the project? | IV
Aviation |  Should a low value shipment take priority from a regular customer or a high value shipment from a infrequent customer? | V
Biology |  Can you explain the mechanisms by which habitat fragmentation affects biodiversity and ecosystem functioning, and provide examples of effective strategies for mitigating these impacts? | III,VI
Business |  If your supplier can give you a discount for a whole yearly production, how can we take this deal without affecting our budget in a critical way? | V
Chemistry |  Why does gallic acid have an affinity with trivalent iron ions? | I
Classical Studies |  If researchers found a new method to unroll the Herculanum papyri, would it be fair to try it on the actual papyrus, given that it could potentially destroy it? | V
Climate Science |  If an imidazolium based ionic liquid were to be released into the environment through the aquatic compartment, what species would be affected, if any? | II,III,V
Criminology |  Mr X is an 18 year old first time offender involved in a burglary where he acted as a lookout. Which category about this information be placed under? | V
Culinary Arts |  If mezcal production in the Valley of Mexico posits the distilling of mezcal can be traced back to ancient times, how could this be attained before the arrival of the Spaniards? | V
Economics |  Can you summarize the current economic policies and strategies of the top five global superpowers and their potential impact on the global market? | I
Education |  Can music therapy impact a child with autism if they have noise sensory issues? | V
Engineering and Technology |  How different will licensing a small modular reactor be as compared to licensing traditional large nuclear power plants? | VII
Environmental Science |  Does floating solar panels minimize the risk of eutrophication or they are more trouble than their worth? | I
Geography |  How can we overcome the limitations of remote sensing data, such as low spatial resolution and limited spectral bands? | IV
Healthcare/Medicine |  If a 48 year old woman is found to have an esophageal carcinoma that invades the muscularis propria and has regional lymph node metastases but no distant metastasis, what is her stage of cancer and what are possible recommended treatments? | I,III
History |  To what extent is JFK’s legacy written from sympathy because of his assassination? | II,VII
Journalism |  How many sources you must have before printing a story? | I
Law |  Can direct evidence in a case that has been obtained illegally be considered by the court in some cases if it directly points to the defendant’s guilt? | I
Linguistics |  What are the attitudes of Received Pronunciation in the United States? | II
Literature |  How would one go about researching the role of the mother represented in Anne Sexton’s 1971 poetry volume "Transformations"? | IV, VI
Mathematics |  Do you think there is a relation between Frobenius numbers and the Kawamata conjecture for weighted complete intersections? | III, VII
Military or Law Enforcement |  If you get anthrax poisoning during a mission, which chemical agent should you use to neutralise the poison? | I
Music |  What exercises would you do in a singing class with a teenager with puberphonia? | IV
Philosophy |  How does modern neuroscience support and reject a computational theory of mind? | III
Physics & Astronomy |  Standard Model does not contain enough CP violating phenomena in order to explain baryon asymmetry. Suppose the existence of such phenomena. Can you propose a way to experimentally observe them? | V
Political Science |  Despite the fact that IPCC was formed in 1988, several studies have showed that argubaly more than 50% of all carbon emissions in history have been released since 1988. What does this show about IPCC and developed countries’ efforts? | VII
Psychology |  How can counselling psychologists effectively and appropriately incorporate use of self into therapy? | III,IV,VII
Sociology |  Which factors strengthen social cohesion within societies? | VII
Theology |  Is there any justification for the use of violence in the New Testament? | I
Visual Arts |  Tell me the step by step process of recycling a canvas. | III

Table 14: Examples from ExpertQA, showing an example from every field included
in the dataset. $0\%$ $10\%$ $20\%$ $30\%$ $40\%$ $50\%$ $60\%$ $70\%$ $80\%$
$90\%$ $100\%$ Visual ArtsTheologySociologyPsychologyPolitical SciencePhysics
and AstronomyPhilosophyMusicMilitary or Law
EnforcementMathematicsLiteratureLinguisticsLawJournalismHistoryMedicineGeographyEnvironmental
ScienceEngineering and TechnologyEducationEconomicsCulinary
ArtsCriminologyClimate ScienceClassical
StudiesChemistryBusinessBiologyAviationArchitectureAnthropology$7.4$ $16.7$
$7.2$ $8.6$ $1.9$ $7.7$ $5.2$ $9.3$ $11.9$ $9.8$ $11.9$ $6.2$ $20$ $13.6$
$5.7$ $16.7$ $7.9$ $6.5$ $5.3$ $16.4$ $13.3$ $10.5$ $1.8$ $11.6$ $3.8$ $3.1$
$11.5$ $3.7$ $8.4$ $4.8$ $4$ $12.1$ $1.9$ $3.5$ $6.7$ $9.8$ $10.7$ $6.9$ $3.7$
$5.3$ $4.2$ $5.3$ $3.2$ $8.8$ $4.9$ $15.4$ $5.3$ $10$ $9.8$ $3.3$ $6.1$ $9.4$
$3.5$ $11.1$ $33.7$ $25$ $33.3$ $28.8$ $27.6$ $34$ $30.8$ $34.5$ $26.7$ $26.2$
$24.4$ $32.1$ $33.9$ $40$ $27.2$ $30.1$ $25$ $29$ $33.3$ $24.6$ $27.9$ $40$
$38.5$ $31.6$ $30$ $27$ $32.2$ $29.6$ $28.1$ $33.3$ $25.9$ $9.5$ $25$ $4.8$
$4.8$ $5.2$ $1.9$ $7.7$ $8.6$ $8$ $14.3$ $4.9$ $2.4$ $6.2$ $6.7$ $4.9$ $10.4$
$16.7$ $10.5$ $11.5$ $11.4$ $8.2$ $7.7$ $20$ $10.4$ $9.1$ $12.9$ $18.8$ $3.5$
$3.7$ $10.5$ $16.7$ $16$ $15.5$ $9.4$ $15.4$ $6.9$ $20$ $16.7$ $14.6$ $13.1$
$10$ $6.7$ $17.3$ $13.8$ $20.8$ $10.5$ $13.6$ $15.8$ $11.5$ $7.7$ $10.5$ $10$
$12.9$ $5.8$ $16.7$ $12.5$ $11.5$ $18.5$ $15.8$ $25$ $16.7$ $24$ $15.5$ $20.8$
$26.9$ $22.4$ $13.3$ $21.4$ $19.5$ $14.3$ $18.5$ $6.7$ $23.5$ $16.9$ $8.3$
$15.8$ $19$ $25.4$ $18$ $33.3$ $23.1$ $21.1$ $20$ $19$ $20.7$ $16.7$ $15.6$
$23$ $25.9$ $14.7$ $25$ $7.1$ $15.2$ $15.5$ $30.2$ $11.5$ $19$ $16$ $9.5$
$17.1$ $15.5$ $18.5$ $20$ $9.9$ $17.9$ $8.3$ $21.1$ $12.9$ $8.8$ $13.1$ $13.3$
$7.7$ $21.1$ $10$ $19$ $17.4$ $14.4$ $12.5$ $13.8$ $11.1$ Type IType IIType
IIIType IVType VType VIType VII Figure 9: The distribution of question types
across all fields included in ExpertQA. Figure 10: Label distribution of claim
properties across different fields for all systems. Figure 11: Label
distribution of claim properties across different question types for all
systems.