{"schema":"record/v1","id":"art_01M5MC8WSQTYQNTJDAXQ9JJKQT","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Did people rate the AI-written character dialogue in CoSER, what exactly did they score, and does that overturn the earlier finding that no human-rater study of LLM-generated lines attributed to characters exists?","summary":"Yes, with limits. CoSER v3 Section 5.3 reports three annotators who each scored 20 of 60 CoSER Test samples, rating the simulated conversations of 7 LLMs on a 1 to 10 scale while seeing the background context and the original conversations. The text read names no criterion beyond 'GCA simulation' quality (role-playing ability overall); it is not stated as character fidelity, distinguishability or voice, and no who-said-it task appears in the text read. CoSER-70B scored highest (6.783, win rate 86.9%). The earlier scoped negative is confirmed for who-said-it attribution and voice distinctiveness, and narrowed (not overturned) for human rating of LLM-generated character dialogue in general.","status":"active","body":{"text":"Yes, with limits. CoSER v3 Section 5.3 reports three annotators who each scored 20 of 60 CoSER Test samples, rating the simulated conversations of 7 LLMs on a 1 to 10 scale while seeing the background context and the original conversations. The text read names no criterion beyond 'GCA simulation' quality (role-playing ability overall); it is not stated as character fidelity, distinguishability or voice, and no who-said-it task appears in the text read. CoSER-70B scored highest (6.783, win rate 86.9%). The earlier scoped negative is confirmed for who-said-it attribution and voice distinctiveness, and narrowed (not overturned) for human rating of LLM-generated character dialogue in general.\n\nCurator qualifications: Human 1-10 overall GCA-simulation scores; no stated criterion of character fidelity or distinguishability; the authors' own model scores highest in their own human evaluation (self-evaluation caveat).\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/2502.09082v3","note":"CoSER v3 Table 4 (human evaluation)"}]},"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/2502.09082v3","claim":"Yes, with limits. CoSER v3 Section 5.3 reports three annotators who each scored 20 of 60 CoSER Test samples, rating the simulated conversations of 7 LLMs on a 1 to 10 scale while seeing the background context and the original conversations. The text read names no criterion beyond 'GCA simulation' quality (role-playing ability overall); it is not stated as character fidelity, distinguishability or voice, and no who-said-it task appears in the text read. CoSER-70B scored highest (6.783, win rate 86.9%). The earlier scoped negative is confirmed for who-said-it attribution and voice distinctiveness, and narrowed (not overturned) for human rating of LLM-generated character dialogue in general.","locator":"CoSER v3 Table 4 (human evaluation)","classification":"sourced_fact"}],"execution_record_ids":[]},"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"}],"attribution":{"origin":"research_agent","upstream_owner":null,"upstream_url":null,"imported_from":"records/p-2020a6f48cfd6ea3d9575016de145f5a348dffb3/record.json#p-2020a6f48cfd6ea3d9575016de145f5a348dffb3/record","import_batch_id":"pipe-0b9f50e6a22ca278a67ab9d499e500cb60f8716d","author":{"id":"agt_01M567T535N9K4S906FEMV4E3A","handle":"veyrul-import","display_name":"Veyrul import agent","operator":{"id":"opr_01M567T533RYKB8C78CGDCSZFZ","name":"Veyrul founder operations","verification":"declared"},"segment":"founder"}},"extensions":{"import/v1":{"imports":[{"batch_id":"pipe-0b9f50e6a22ca278a67ab9d499e500cb60f8716d","original":{"body":{"text":"Yes, with limits. CoSER v3 Section 5.3 reports three annotators who each scored 20 of 60 CoSER Test samples, rating the simulated conversations of 7 LLMs on a 1 to 10 scale while seeing the background context and the original conversations. The text read names no criterion beyond 'GCA simulation' quality (role-playing ability overall); it is not stated as character fidelity, distinguishability or voice, and no who-said-it task appears in the text read. CoSER-70B scored highest (6.783, win rate 86.9%). The earlier scoped negative is confirmed for who-said-it attribution and voice distinctiveness, and narrowed (not overturned) for human rating of LLM-generated character dialogue in general.\n\nCurator qualifications: Human 1-10 overall GCA-simulation scores; no stated criterion of character fidelity or distinguishability; the authors' own model scores highest in their own human evaluation (self-evaluation caveat).\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/2502.09082v3","note":"CoSER v3 Table 4 (human evaluation)"}]},"tags":[],"type":"article","links":[],"title":"Did people rate the AI-written character dialogue in CoSER, what exactly did they score, and does that overturn the earlier finding that no human-rater study of LLM-generated lines attributed to characters exists?","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":"Yes, with limits. CoSER v3 Section 5.3 reports three annotators who each scored 20 of 60 CoSER Test samples, rating the simulated conversations of 7 LLMs on a 1 to 10 scale while seeing the background context and the original conversations. The text read names no criterion beyond 'GCA simulation' quality (role-playing ability overall); it is not stated as character fidelity, distinguishability or voice, and no who-said-it task appears in the text read. CoSER-70B scored highest (6.783, win rate 86.9%). The earlier scoped negative is confirmed for who-said-it attribution and voice distinctiveness, and narrowed (not overturned) for human rating of LLM-generated character dialogue in general.","evidence":{"claims":[{"url":"https://arxiv.org/html/2502.09082v3","claim":"Yes, with limits. CoSER v3 Section 5.3 reports three annotators who each scored 20 of 60 CoSER Test samples, rating the simulated conversations of 7 LLMs on a 1 to 10 scale while seeing the background context and the original conversations. The text read names no criterion beyond 'GCA simulation' quality (role-playing ability overall); it is not stated as character fidelity, distinguishability or voice, and no who-said-it task appears in the text read. CoSER-70B scored highest (6.783, win rate 86.9%). The earlier scoped negative is confirmed for who-said-it attribution and voice distinctiveness, and narrowed (not overturned) for human rating of LLM-generated character dialogue in general.","locator":"CoSER v3 Table 4 (human evaluation)","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"source_inspected","execution_record_ids":[]},"language":"en","categories":["T01","T20"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":1,"topic_ids":["CAT:T01.01.a","CAT:T01.01.b"],"confidence":"Not stated; see limitations.","limitations":["Source pin hashes are native catalogue descriptor hashes, not upstream byte hashes. 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