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<title>[2502.17125] LettuceDetect: A Hallucination Detection Framework for RAG Applications</title>
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<meta property=og:description content="Retrieval Augmented Generation (RAG) systems remain vulnerable to hallucinated answers despite incorporating external knowledge sources. We present LettuceDetect a framework that addresses two critical limitations in existing hallucination detection methods: (1) the context window constraints of traditional encoder-based methods, and (2) the computational inefficiency of LLM based approaches. Building on ModernBERT's extended context capabilities (up to 8k tokens) and trained on the RAGTruth benchmark dataset, our approach outperforms all previous encoder-based models and most prompt-based models, while being approximately 30 times smaller than the best models. LettuceDetect is a token-classification model that processes context-question-answer triples, allowing for the identification of unsupported claims at the token level. Evaluations on the RAGTruth corpus demonstrate an F1 score of 79.22% for example-level detection, which is a 14.8% improvement over Luna, the previous state-of-the-art encoder-based architecture. Additionally, the system can process 30 to 60 examples per second on a single GPU, making it more practical for real-world RAG applications.">
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<meta name=citation_title content="LettuceDetect: A Hallucination Detection Framework for RAG Applications"><meta name=citation_author content="Kovács, Ádám"><meta name=citation_author content="Recski, Gábor"><meta name=citation_date content=2025/02/24><meta name=citation_online_date content=2025/02/24><meta name=citation_pdf_url content=https://arxiv.org/pdf/2502.17125><meta name=citation_arxiv_id content=2502.17125><meta name=citation_abstract content="Retrieval Augmented Generation (RAG) systems remain vulnerable to hallucinated answers despite incorporating external knowledge sources. We present LettuceDetect a framework that addresses two critical limitations in existing hallucination detection methods: (1) the context window constraints of traditional encoder-based methods, and (2) the computational inefficiency of LLM based approaches. Building on ModernBERT's extended context capabilities (up to 8k tokens) and trained on the RAGTruth benchmark dataset, our approach outperforms all previous encoder-based models and most prompt-based models, while being approximately 30 times smaller than the best models. LettuceDetect is a token-classification model that processes context-question-answer triples, allowing for the identification of unsupported claims at the token level. Evaluations on the RAGTruth corpus demonstrate an F1 score of 79.22% for example-level detection, which is a 14.8% improvement over Luna, the previous state-of-the-art encoder-based architecture. Additionally, the system can process 30 to 60 examples per second on a single GPU, making it more practical for real-world RAG applications.">
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<h1>Computer Science &gt; Computation and Language</h1>
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<strong>arXiv:2502.17125</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 24 Feb 2025]</div>
<h1 class="title mathjax"><span class="descriptor sf-hidden">Title:</span>LettuceDetect: A Hallucination Detection Framework for RAG Applications</h1>
<div class=authors><span class="descriptor sf-hidden">Authors:</span><a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Kov%C3%A1cs,+%C3%81" rel=nofollow>Ádám Kovács</a>, <a href="https://arxiv.org/search/cs?searchtype=author&amp;query=Recski,+G" rel=nofollow>Gábor Recski</a></div> <div id=download-button-info class=sf-hidden hidden>View a PDF of the paper titled LettuceDetect: A Hallucination Detection Framework for RAG Applications, by \'Ad\'am Kov\'acs and 1 other authors</div>
<a class=mobile-submission-download href=https://arxiv.org/pdf/2502.17125>View PDF</a>
<a class=mobile-submission-download href=https://arxiv.org/html/2502.17125v1>HTML (experimental)</a>
<blockquote class="abstract mathjax">
<span class="descriptor sf-hidden">Abstract:</span>Retrieval Augmented Generation (RAG) systems remain vulnerable to hallucinated answers despite incorporating external knowledge sources. We present LettuceDetect a framework that addresses two critical limitations in existing hallucination detection methods: (1) the context window constraints of traditional encoder-based methods, and (2) the computational inefficiency of LLM based approaches. Building on ModernBERT's extended context capabilities (up to 8k tokens) and trained on the RAGTruth benchmark dataset, our approach outperforms all previous encoder-based models and most prompt-based models, while being approximately 30 times smaller than the best models. LettuceDetect is a token-classification model that processes context-question-answer triples, allowing for the identification of unsupported claims at the token level. Evaluations on the RAGTruth corpus demonstrate an F1 score of 79.22% for example-level detection, which is a 14.8% improvement over Luna, the previous state-of-the-art encoder-based architecture. Additionally, the system can process 30 to 60 examples per second on a single GPU, making it more practical for real-world RAG applications.
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<table summary="Additional metadata"> <tbody><tr>
<td class="tablecell label">Comments:</td>
<td class="tablecell comments mathjax">6 pages</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>; Artificial Intelligence (cs.AI)</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/2502.17125>arXiv:2502.17125</a> [cs.CL]</span></td>
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<td class="tablecell label">&nbsp;</td>
<td class="tablecell arxividv">(or <span class=arxivid>
<a href=https://arxiv.org/abs/2502.17125v1>arXiv:2502.17125v1</a> [cs.CL]</span> for this version)
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</tr>
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<h2>Submission history</h2> From: Ádám Kovács [<a href=https://arxiv.org/show-email/5844766b/2502.17125 rel=nofollow>view email</a>] <br> <strong>[v1]</strong>
Mon, 24 Feb 2025 13:11:47 UTC (1,188 KB)<br>
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