{"schema":"record/v1","id":"art_01M5MC7JK228BZ5XG79VRH8G42","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Can an LLM judge reliably say which character is speaking, and what does that imply for using an LLM to check character voice?","summary":"PersonaEval (arXiv 2508.10014v1, 6 Aug 2025) gives LLM evaluators dialogue context plus four or five described candidate roles and asks who the second speaker is. The paper reports the best LLM at around 69% top-1 accuracy against 90.8% for 20 human volunteers on a hard sample, and says models lean on speaking style while humans use addressing and intent. Items are human-authored (English novels, Chinese scripts, English explainer videos), so the result says how judges treat existing text, not how they treat generated dialogue.","status":"active","body":{"text":"PersonaEval (arXiv 2508.10014v1, 6 Aug 2025) gives LLM evaluators dialogue context plus four or five described candidate roles and asks who the second speaker is. The paper reports the best LLM at around 69% top-1 accuracy against 90.8% for 20 human volunteers on a hard sample, and says models lean on speaking style while humans use addressing and intent. Items are human-authored (English novels, Chinese scripts, English explainer videos), so the result says how judges treat existing text, not how they treat generated dialogue.\n\nCurator qualifications: PersonaEval: best LLM ~69% top-1 vs 90.8% humans on a hard sample; models lean on style, humans on addressing and intent.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/2508.10014v1","note":"candidate evidence excerpts"},{"url":"https://raw.githubusercontent.com/maple-zhou/PersonaEval/19f7568a84f52e206dccdf67146235c8376cec9c/README.md","note":"candidate evidence excerpts"}]},"categories":["T01","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/html/2508.10014v1","claim":"PersonaEval (arXiv 2508.10014v1, 6 Aug 2025) gives LLM evaluators dialogue context plus four or five described candidate roles and asks who the second speaker is. 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The paper reports the best LLM at around 69% top-1 accuracy against 90.8% for 20 human volunteers on a hard sample, and says models lean on speaking style while humans use addressing and intent. Items are human-authored (English novels, Chinese scripts, English explainer videos), so the result says how judges treat existing text, not how they treat generated dialogue.\n\nCurator qualifications: PersonaEval: best LLM ~69% top-1 vs 90.8% humans on a hard sample; models lean on style, humans on addressing and intent.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/2508.10014v1","note":"candidate evidence excerpts"},{"url":"https://raw.githubusercontent.com/maple-zhou/PersonaEval/19f7568a84f52e206dccdf67146235c8376cec9c/README.md","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"Can an LLM judge reliably say which character is speaking, and what does that imply for using an LLM to check character voice?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"PersonaEval (arXiv 2508.10014v1, 6 Aug 2025) gives LLM evaluators dialogue context plus four or five described candidate roles and asks who the second speaker is. The paper reports the best LLM at around 69% top-1 accuracy against 90.8% for 20 human volunteers on a hard sample, and says models lean on speaking style while humans use addressing and intent. Items are human-authored (English novels, Chinese scripts, English explainer videos), so the result says how judges treat existing text, not how they treat generated dialogue.","evidence":{"claims":[{"url":"https://arxiv.org/html/2508.10014v1","claim":"PersonaEval (arXiv 2508.10014v1, 6 Aug 2025) gives LLM evaluators dialogue context plus four or five described candidate roles and asks who the second speaker is. The paper reports the best LLM at around 69% top-1 accuracy against 90.8% for 20 human volunteers on a hard sample, and says models lean on speaking style while humans use addressing and intent. 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