v1</p>\n","updatedAt":"2026-06-02T14:43:38.079Z","author":{"_id":"63578f828ed056fa1cccb7a4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63578f828ed056fa1cccb7a4/WH1M3yyAwl9AcZdnRZqyj.png","fullname":"yubol-bobo","name":"yubol","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"pt","probability":0.9997303485870361},"editors":["yubol"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/63578f828ed056fa1cccb7a4/WH1M3yyAwl9AcZdnRZqyj.png"],"reactions":[],"isReport":false}},{"id":"6a1f8a5fb4f9cc018c082110","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":360,"isUserFollowing":false},"createdAt":"2026-06-03T01:58:55.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [AuthTrace: Diagnosing Evidence Construction in Thematically Dense Single-Author Corpora](https://huggingface.co/papers/2605.25382) (2026)\n* [Benchmarking Retrieval Strategies for Biomedical Retrieval-Augmented Generation: A Controlled Empirical Study](https://huggingface.co/papers/2605.02520) (2026)\n* [DrugClaw and DrugAudit: A Primary-Source-Grounded Agent and Authority-Aware Benchmark for Drug-Information Question Answering](https://huggingface.co/papers/2606.01434) (2026)\n* [ASTRA-QA: A Benchmark for Abstract Question Answering over Documents](https://huggingface.co/papers/2605.10168) (2026)\n* [BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detection](https://huggingface.co/papers/2604.10389) (2026)\n* [Iterative Multimodal Retrieval-Augmented Generation for Medical Question Answering](https://huggingface.co/papers/2604.27724) (2026)\n* [Same Ranking, Different Winner: How Scoring Targets Shape LLM Memory Benchmarks](https://huggingface.co/papers/2605.24060) (2026)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"<p>This is an automated message from the <a href=\"https://huggingface.co/librarian-bots\">Librarian Bot</a>. I found the following papers similar to this paper. </p>\n<p>The following papers were recommended by the Semantic Scholar API </p>\n<ul>\n<li><a href=\"https://huggingface.co/papers/2605.25382\">AuthTrace: Diagnosing Evidence Construction in Thematically Dense Single-Author Corpora</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.02520\">Benchmarking Retrieval Strategies for Biomedical Retrieval-Augmented Generation: A Controlled Empirical Study</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2606.01434\">DrugClaw and DrugAudit: A Primary-Source-Grounded Agent and Authority-Aware Benchmark for Drug-Information Question Answering</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.10168\">ASTRA-QA: A Benchmark for Abstract Question Answering over Documents</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.10389\">BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detection</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.27724\">Iterative Multimodal Retrieval-Augmented Generation for Medical Question Answering</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.24060\">Same Ranking, Different Winner: How Scoring Targets Shape LLM Memory Benchmarks</a> (2026)</li>\n</ul>\n<p> Please give a thumbs up to this comment if you found it helpful!</p>\n<p> If you want recommendations for any Paper on Hugging Face checkout <a href=\"https://huggingface.co/spaces/librarian-bots/recommend_similar_papers\">this</a> Space</p>\n<p> You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: <code><span class=\"SVELTE_PARTIAL_HYDRATER contents\" data-target=\"UserMention\" data-props=\"{&quot;user&quot;:&quot;librarian-bot&quot;}\"><span class=\"inline-block\"><span class=\"contents\"><a href=\"/librarian-bot\">@<span class=\"underline\">librarian-bot</span></a></span> </span></span> recommend</code></p>\n","updatedAt":"2026-06-03T01:58:55.904Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":360,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7099631428718567},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.29084","authors":[{"_id":"6a1eec0fe292c1c78ecb10fb","name":"Yubo Li","hidden":false},{"_id":"6a1eec0fe292c1c78ecb10fc","name":"Rema Padman","hidden":false},{"_id":"6a1eec0fe292c1c78ecb10fd","name":"Ramayya Krishnan","hidden":false}],"publishedAt":"2026-05-27T00:00:00.000Z","submittedOnDailyAt":"2026-06-02T00:00:00.000Z","title":"Same Question, Different Source, Different Answer: Auditing Source-Dependence in Medical Multi-Source RAG","submittedOnDailyBy":{"_id":"63578f828ed056fa1cccb7a4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63578f828ed056fa1cccb7a4/WH1M3yyAwl9AcZdnRZqyj.png","isPro":false,"fullname":"yubol-bobo","user":"yubol","type":"user","name":"yubol"},"summary":"A retrieval-augmented generation (RAG) system deployed over a multi-author institutional corpus can give a different answer to the same question depending on which source it retrieves -- a failure mode the dominant single-gold-answer paradigm cannot diagnose. We argue that source-dependence is a missing axis of NLP evaluation, and that auditing it means shifting the unit of evaluation from answer correctness to the inter-source relationship. We make this concrete in transplant patient education, where institutional sources demonstrably disagree, releasing three artefacts: TransplantQA, a benchmark of real patient questions, each answered by grounding generation in multiple institutional handbooks as candidate sources; HERO-QA, a hierarchical retrieval strategy that grounds and audits each answer; and a structured-output judge that scores inter-source relationships on a validated 5-label taxonomy. At scale, better retrieval reveals far more disagreement than prior estimates suggested -- understating its prevalence, not its intensity. The framework is domain-agnostic and transfers to legal and educational RAG: measuring source-dependence is a responsibility for deployed multi-source NLP generally.","upvotes":1,"discussionId":"6a1eec0fe292c1c78ecb10fe","ai_summary":"Retrieval-augmented generation systems exhibit source-dependent responses to identical queries, necessitating a shift from traditional correctness evaluation to analyzing inter-source relationships for multi-source NLP systems.","ai_keywords":["retrieval-augmented generation","source-dependence","NLP evaluation","grounding generation","hierarchical retrieval","structured-output judge","multi-source NLP"],"ai_summary_model":"Qwen/Qwen2.5-Coder-32B-Instruct","organization":{"_id":"691d9a1012cc4d473e1c862f","name":"CarnegieMellonU","fullname":"Carnegie Mellon University","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/68e396f2b5bb631e9b2fac9a/6I146aJvxxlRCEbYFFAeQ.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"63578f828ed056fa1cccb7a4","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63578f828ed056fa1cccb7a4/WH1M3yyAwl9AcZdnRZqyj.png","isPro":false,"fullname":"yubol-bobo","user":"yubol","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"691d9a1012cc4d473e1c862f","name":"CarnegieMellonU","fullname":"Carnegie Mellon University","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/68e396f2b5bb631e9b2fac9a/6I146aJvxxlRCEbYFFAeQ.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2605/2605.29084.md"}">

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