Summary Expert QA Evaluating Factuality and Attribution in Language Models arxiv.org
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One Line
The study assesses the accuracy and source of language models across different domains using the Expert QA system.
Slides
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Key Points
- Language models are being used in various fields, such as medicine and law, but ensuring accurate information supported by reliable sources is crucial.
- Previous studies on factuality and attribution in language models have not focused on domain-specific scenarios.
- The study evaluates factuality and attribution in language models using the Expert QA system.
- Annotators judge the factual correctness of claims based on their expertise, evidence from the system, and minimal internet browsing.
- GPT-4 generates citations to URLs that often lead to trustworthy domains, but the content on these pages is often mismatched.
- The authors evaluate the factuality and attribution labels generated by language models using an NLI classifier as an AutoAIS system.
- The framework of Attributable to Identified Sources (AIS) is proposed for human evaluation of attributions.
- The excerpt includes a list of references and citations from various research papers and publications related to evaluating factuality and attribution in language models.
Summaries
18 word summary
This study evaluates the factuality and attribution of language models using the Expert QA system in various fields.
43 word summary
Language models are being used in various fields, such as medicine and law, but ensuring their accuracy and reliability is crucial. The study evaluates factuality and attribution in language models using the Expert QA system. Annotators judge the factual correctness of claims based
617 word summary
Language models are being used in various fields, such as medicine and law, but ensuring that they provide accurate information supported by reliable sources is crucial. Previous studies on factuality and attribution in language models have not focused on domain-specific scenarios. In this evaluation
The study evaluates the factuality and attribution in language models using the Expert QA system. Retrieve-and-read systems struggle with producing citations for all cite-worthy claims, and high-stakes domains like medicine and law have a large percentage of incomplete attributions and unreliable
The summary is organized into separate paragraphs to distinguish distinct ideas:
Table 1 shows examples from E XPERT QA, including question types and counts. Table 2 categorizes question types according to different information needs. The study collected over 3000 questions
Annotators are asked to judge the factual correctness of claims based on their expertise, evidence from the system, and minimal internet browsing. They are instructed to be conservative in their judgments and label a claim as "Definitely correct" only if every word
This excerpt discusses the evaluation of factuality and attribution in language models. The authors use a prompt from Table 10 and consider commercial systems like BingChat. They sample responses from BingChat and other systems, excluding abstained answers. The number of examples
GPT-4 generates citations to URLs that often lead to trustworthy domains, but the content on these pages is often mismatched. Both prompting and retrieval-augmented systems generate mostly relevant claims, but a significant percentage are not relevant or void claims.
In this study, the authors evaluate the factuality and attribution labels generated by language models. They use the NLI classifier from a previous work as an AutoAIS system to predict attribution labels for claim-evidence pairs. The results show that AutoA
The framework of Attributable to Identified Sources (AIS) is proposed for human evaluation of attributions. Systems still struggle with providing precise attributions for citeworthy statements. Automatic methods for measuring attribution have been explored, including textual entailment models and
This excerpt includes a list of references to various papers and articles related to evaluating factuality and attribution in language models. The references cover topics such as large language models, attributed question answering, improving language models, claim verification, long form question answering, proposition
This excerpt includes a list of references and citations from various research papers and publications related to language models, factuality evaluation, and question answering. The references cover topics such as improved QA-based factual consistency evaluation, legal reasoning in language models, automated fact-check
Several papers related to evaluating factuality and attribution in language models are referenced in this excerpt. These papers cover various aspects of language model evaluation, including automatic evaluation of summaries, collaborative datasets for generative information-seeking, real-world entailment for claims, scientific
This document excerpt includes a list of references to various research papers and technical reports related to evaluating factuality and attribution in language models. The references cover a range of topics such as generating benchmarks for factuality evaluation, cross-lingual question answering, browser
The text includes citations of various research papers and preprints related to language models, conversational question answering, information retrieval, user goals in web search, factoid questions, instruction-following models, transformer memory, language models for dialog applications, fact extraction
The summary of the excerpt is as follows: - The study involved 484 participants from 26 different countries who were considered experts in their fields. - Participants were informed that their annotations would be used to evaluate the capabilities of language models in providing truthful answers
This excerpt is a collection of tables and prompts related to the evaluation of factuality and attribution in language models. Table 9 outlines a prompt for GPT4 and BingChat that instructs users to provide context and support their answers with citations. Table