E xpert QA : Expert-Curated Questions and Attributed Answers (2024)

Abstract: As language models are adopted 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. 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 attribution and factuality has not focused on analyzing these characteristics of language model outputs in domain-specific scenarios. In this work, we conduct human evaluation of responses from a few representative systems along various axes of attribution and factuality, by bringing domain experts in the loop. Specifically, we 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. In addition, we ask experts to improve upon responses from language models. The output of our analysis is 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.

## introduction 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 @xcite and researchers are using them for faster literature surveys @xcite @xcite . 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 factual? citeworthy? informative? supported? a randomized controlled trial found that cryotherapy could reduce sensory neuropathy symptoms, the need for dose reduction, and the incidence of severe peripheral neuropathy [2]. ## expert-in-the-loop evaluation the evaluation is conducted in multiple stages described 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, costs and interfaces, are in appendix a. ## systems evaluated we now describe the classes of systems from which we sampled responses to questions. all systems we evaluated produce an answer string and attributions in the form of in-line citations. attributions are returned as urls or passages along with urls from where they are retrieved. experimental details such as prompts are in appendix b. llm as generator + retriever. in this paradigm, we prompt large language models in a closed-book fashion @xcite to generate an answer with in-line citations where they provide urls for each citation. this means that the model essentially has to generate a url from its parametric memory. we consider gpt-4 as the llm from which we sample responses (gpt4). 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 @xcite (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 prompt a model to use the retrieved evidence to answer the question @xcite . as our attribution corpus, we again consider sphere @xcite and google search results (rr_gs_gpt4). we use bm25 @xcite for retrieving from sphere. we then generate an answer using gpt-4, providing the retrieved evidence as context. the model is instructed to generate in-line citations for each sentence, which refer to the passages in the context. commercial. we also consider commercial systems such as bingchat. 3 we sample responses using the balanced mode of bingchat (bing_chat). ## automatic estimation of attribution and factuality prior work has proposed automatic methods to predict attribution and factuality of claims. we evaluate how reliably these methods reflect the expert labels in our collected data. we evaluate the effectiveness of these methods for claims in expertqa. in both cases, we observe that current methods show high precision but low recall when compared with human judgements.. ## conclusion and future work our evaluation study suggests that although large language models show a lot of promise for aiding domain experts, there is large ground to cover in addressing the information needs of experts with factual and verifiable answers @xcite . experts, on the other hand, should take responses from these systems with caution, because although attributed responses can seem trustworthy, the supporting references can often be inadequate to support claims. we hope that our benchmark, ex-pertqa, can benefit the community in building improved methods for attribution & factuality estimation, and long-form question answering.