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<title>[2305.11747] HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models</title>
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<meta name=description content="Abstract page for arXiv paper 2305.11747: HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models"><meta property=og:type content=website>
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<meta property=og:description content="Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, i.e., content that conflicts with the source or cannot be verified by the factual knowledge. To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation benchmark for Large Language Models (HaluEval), a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination. To generate these samples, we propose a ChatGPT-based two-step framework, i.e., sampling-then-filtering. Besides, we also hire some human labelers to annotate the hallucinations in ChatGPT responses. The empirical results suggest that ChatGPT is likely to generate hallucinated content in specific topics by fabricating unverifiable information (i.e., about $19.5\%$ responses). Moreover, existing LLMs face great challenges in recognizing the hallucinations in texts. However, our experiments also prove that providing external knowledge or adding reasoning steps can help LLMs recognize hallucinations. Our benchmark can be accessed at https://github.com/RUCAIBox/HaluEval.">
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<meta name=citation_title content="HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models"><meta name=citation_author content="Li, Junyi"><meta name=citation_author content="Cheng, Xiaoxue"><meta name=citation_author content="Zhao, Wayne Xin"><meta name=citation_author content="Nie, Jian-Yun"><meta name=citation_author content="Wen, Ji-Rong"><meta name=citation_date content=2023/05/19><meta name=citation_online_date content=2023/10/23><meta name=citation_pdf_url content=https://arxiv.org/pdf/2305.11747><meta name=citation_arxiv_id content=2305.11747><meta name=citation_abstract content="Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, i.e., content that conflicts with the source or cannot be verified by the factual knowledge. To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation benchmark for Large Language Models (HaluEval), a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination. To generate these samples, we propose a ChatGPT-based two-step framework, i.e., sampling-then-filtering. Besides, we also hire some human labelers to annotate the hallucinations in ChatGPT responses. The empirical results suggest that ChatGPT is likely to generate hallucinated content in specific topics by fabricating unverifiable information (i.e., about $19.5\%$ responses). Moreover, existing LLMs face great challenges in recognizing the hallucinations in texts. However, our experiments also prove that providing external knowledge or adding reasoning steps can help LLMs recognize hallucinations. Our benchmark can be accessed at https://github.com/RUCAIBox/HaluEval.">
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<h1>Computer Science &gt; Computation and Language</h1>
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<strong>arXiv:2305.11747</strong> (cs)
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<style>#abs{font-family:"Lucida Grande",Helvetica,Arial,sans-serif!important}#abs h1.title{display:block;font-size:1.8em!important;font-weight:700;margin-block-end:12px;margin-block-start:12px;margin-bottom:12px;margin-inline-end:0px;margin-inline-start:20px;margin-left:20px;margin-right:0px;margin-top:12px}#abs div.authors{font-size:1.2em;line-height:24px;margin-bottom:8px;margin-left:20px;margin-right:0px;margin-top:8px}#abs div.dateline{font-size:0.9em;font-style:italic;margin-bottom:6.5px;margin-left:20px;margin-right:0px;margin-top:6.5px}#abs blockquote.abstract{font-size:1.05em;margin-block-end:21.6px;margin-block-start:14.4px;margin-bottom:21.6px;margin-inline-end:40px;margin-inline-start:40px;background-color:white;border-left:0px;padding:0px}#abs div.metatable{font-size:0.95em!important;margin-bottom:19px;margin-left:20px;margin-right:0px;margin-top:0px;border:0px;padding:0px}#abs div.metatable tbody{vertical-align:middle}#abs tr{margin-top:0px;margin-bottom:0px}#abs td.tablecell{padding-top:0px;padding-bottom:0px;padding-right:6.5px;padding-left:0px;vertical-align:top;font-size:0.95em!important;margin-top:0px;margin-bottom:0px;border:0px}#abs td.tablecell.label{font-weight:400!important}#abs span.primary-subject{font-weight:700}#abs span.arxivid{font-weight:700}</style>
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[Submitted on 19 May 2023 (<a href=https://arxiv.org/abs/2305.11747v1>v1</a>), last revised 23 Oct 2023 (this version, v3)]</div>
<h1 class="title mathjax"><span class="descriptor sf-hidden">Title:</span>HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models</h1>
<div class=authors><span class="descriptor sf-hidden">Authors:</span><a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Li,+J" rel=nofollow>Junyi Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Cheng,+X" rel=nofollow>Xiaoxue Cheng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Zhao,+W+X" rel=nofollow>Wayne Xin Zhao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Nie,+J" rel=nofollow>Jian-Yun Nie</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Wen,+J" rel=nofollow>Ji-Rong Wen</a></div> <div id=download-button-info class=sf-hidden hidden>View a PDF of the paper titled HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models, by Junyi Li and 3 other authors</div>
<a class=mobile-submission-download href=https://arxiv.org/pdf/2305.11747>View PDF</a>
<blockquote class="abstract mathjax">
<span class="descriptor sf-hidden">Abstract:</span>Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, i.e., content that conflicts with the source or cannot be verified by the factual knowledge. To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation benchmark for Large Language Models (HaluEval), a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination. To generate these samples, we propose a ChatGPT-based two-step framework, i.e., sampling-then-filtering. Besides, we also hire some human labelers to annotate the hallucinations in ChatGPT responses. The empirical results suggest that ChatGPT is likely to generate hallucinated content in specific topics by fabricating unverifiable information (i.e., about <span class=MathJax_Preview>19.5\%</span><span class="MathJax MathJax_Processing sf-hidden" id=MathJax-Element-1-Frame tabindex=0></span> responses). Moreover, existing LLMs face great challenges in recognizing the hallucinations in texts. However, our experiments also prove that providing external knowledge or adding reasoning steps can help LLMs recognize hallucinations. Our benchmark can be accessed at <a href=https://github.com/RUCAIBox/HaluEval rel="external noopener nofollow" class="link-external link-https">this https URL</a>.
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<td class="tablecell label">Comments:</td>
<td class="tablecell comments mathjax">Accepted to EMNLP 2023 Main Conference (Long Paper)</td>
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<td class="tablecell label">Subjects:</td>
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<span class=primary-subject>Computation and Language (cs.CL)</span></td>
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<td class="tablecell label">Cite as:</td>
<td class="tablecell arxivid"><span class=arxivid><a href=https://arxiv.org/abs/2305.11747>arXiv:2305.11747</a> [cs.CL]</span></td>
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<td class="tablecell arxividv">(or <span class=arxivid>
<a href=https://arxiv.org/abs/2305.11747v3>arXiv:2305.11747v3</a> [cs.CL]</span> for this version)
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<td class="tablecell arxivdoi"> <a href=https://doi.org/10.48550/arXiv.2305.11747 id=arxiv-doi-link>https://doi.org/10.48550/arXiv.2305.11747</a><div class=button-and-tooltip>
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<div class=submission-history>
<h2>Submission history</h2> From: Junyi Li [<a href=https://arxiv.org/show-email/f59aab9c/2305.11747 rel=nofollow>view email</a>] <br> <strong><a href=https://arxiv.org/abs/2305.11747v1 rel=nofollow>[v1]</a></strong>
Fri, 19 May 2023 15:36:27 UTC (686 KB)<br>
<strong><a href=https://arxiv.org/abs/2305.11747v2 rel=nofollow>[v2]</a></strong>
Mon, 22 May 2023 13:36:09 UTC (687 KB)<br>
<strong>[v3]</strong>
Mon, 23 Oct 2023 01:49:32 UTC (689 KB)<br>
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