{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/benchmarking-llm-faithfulness-in-rag-with","title":"Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards","arxiv_id":"2505.04847","date":"2025-05-07","proceeding":null,"authors":["Manveer Singh Tamber","Forrest Sheng Bao","Chenyu Xu","Ge Luo","Suleman Kazi","Minseok Bae","Miaoran Li","Ofer Mendelevitch","Renyi Qu","Jimmy Lin"],"abstract":"Hallucinations remain a persistent challenge for LLMs. RAG aims to reduce hallucinations by grounding responses in contexts. However, even when provided context, LLMs still frequently introduce unsupported information or contradictions. This paper presents our efforts to measure LLM hallucinations with a focus on summarization tasks, assessing how often various LLMs introduce hallucinations when summarizing documents. We discuss Vectara's existing LLM hallucination leaderboard, based on the Hughes Hallucination Evaluation Model (HHEM). While HHEM and Vectara's Hallucination Leaderboard have garnered great research interest, we examine challenges faced by HHEM and current hallucination detection methods by analyzing the effectiveness of these methods on existing hallucination datasets. To address these limitations, we propose FaithJudge, an LLM-as-a-judge approach guided by few-shot human hallucination annotations, which substantially improves automated LLM hallucination evaluation over current methods. We introduce an enhanced hallucination leaderboard centered on FaithJudge, alongside our current hallucination leaderboard, enabling more reliable benchmarking of LLMs for hallucinations in RAG.","url_abs":"https://arxiv.org/abs/2505.04847v1","url_pdf":"https://arxiv.org/pdf/2505.04847v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"benchmarking-llm-faithfulness-in-rag-with","repo_url":"https://github.com/vectara/FaithJudge","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"hallucination","task_name":"Hallucination"},{"task_slug":"hallucination-evaluation","task_name":"Hallucination Evaluation"},{"task_slug":"rag","task_name":"RAG"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bart","method_name":"BART"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"focus","method_name":"Focus"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"rag","method_name":"RAG"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2505.04847","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.04847"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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