{"schema":"record/v1","id":"art_01M5MC7DS2Z6J8C1BTG9JJJR7J","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Which English-language role-play or persona benchmarks exist, and what does each one actually score (is character-to-character voice distinctiveness among the scored things)?","summary":"Four benchmark-style resources were read. RoleBench scores reference overlap (ROUGE-L) and judge/human win rates for single-turn answers in a role; PersonaGym scores five rubric tasks per demographic persona with an LLM-judge ensemble; PersonaEval scores whether a judge can name the speaker in human-written dialogue; the Speaker Verification work is the only one read that defines a cross-character measure (Distinction Score) on generated lines. CharacterEval is Chinese-only on its abstract page. None of the four reports a human-rated test of whether generated characters sound different from each other.","status":"active","body":{"text":"Four benchmark-style resources were read. RoleBench scores reference overlap (ROUGE-L) and judge/human win rates for single-turn answers in a role; PersonaGym scores five rubric tasks per demographic persona with an LLM-judge ensemble; PersonaEval scores whether a judge can name the speaker in human-written dialogue; the Speaker Verification work is the only one read that defines a cross-character measure (Distinction Score) on generated lines. CharacterEval is Chinese-only on its abstract page. None of the four reports a human-rated test of whether generated characters sound different from each other.\n\nCurator qualifications: Four resources measure different things: RoleBench (ROUGE-L and win rates, single turn), PersonaGym (rubric tasks with LLM-judge ensemble), PersonaEval (judge names the speaker in human dialogue), Speaker Verification (only cross-character Distinction Score).\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/2310.00746v3","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2407.18416v5","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2508.10014v1","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2401.01275","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2606.22454","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2405.10150","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2405.10150v2","note":"candidate evidence excerpts"}]},"categories":["T01","T18","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/2310.00746v3","claim":"Four benchmark-style resources were read. 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RoleBench scores reference overlap (ROUGE-L) and judge/human win rates for single-turn answers in a role; PersonaGym scores five rubric tasks per demographic persona with an LLM-judge ensemble; PersonaEval scores whether a judge can name the speaker in human-written dialogue; the Speaker Verification work is the only one read that defines a cross-character measure (Distinction Score) on generated lines. CharacterEval is Chinese-only on its abstract page. None of the four reports a human-rated test of whether generated characters sound different from each other.\n\nCurator qualifications: Four resources measure different things: RoleBench (ROUGE-L and win rates, single turn), PersonaGym (rubric tasks with LLM-judge ensemble), PersonaEval (judge names the speaker in human dialogue), Speaker Verification (only cross-character Distinction Score).\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/2310.00746v3","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2407.18416v5","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2508.10014v1","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2401.01275","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2606.22454","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2405.10150","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2405.10150v2","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"Which English-language role-play or persona benchmarks exist, and what does each one actually score (is character-to-character voice distinctiveness among the scored things)?","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":"Four benchmark-style resources were read. RoleBench scores reference overlap (ROUGE-L) and judge/human win rates for single-turn answers in a role; PersonaGym scores five rubric tasks per demographic persona with an LLM-judge ensemble; PersonaEval scores whether a judge can name the speaker in human-written dialogue; the Speaker Verification work is the only one read that defines a cross-character measure (Distinction Score) on generated lines. CharacterEval is Chinese-only on its abstract page. None of the four reports a human-rated test of whether generated characters sound different from each other.","evidence":{"claims":[{"url":"https://arxiv.org/html/2310.00746v3","claim":"Four benchmark-style resources were read. 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