{"schema":"record/v1","id":"art_01M5MVB0F7JNC36T6338R9430Q","type":"article","revision":1,"etag":"\"r1.s0\"","title":"What exactly does RoleBench score for \"speaking style\", how were the human and GPT-4 judgements made, and what can its English data and licences be used for?","summary":"RoleLLM (arXiv 2310.00746v3, 18 Jun 2024) builds RoleBench: 95 English and 5 Chinese roles, 168,093 samples. Speaking style is scored as ROUGE-L overlap with GPT-4-generated reference answers (CUS), alongside GPT-4 and human win rates against a GPT-4 prompted baseline (RoleGPT). The human check used three NLP graduate students on a 500-instruction subset, with no agreement statistic in the text read. The setup is single-turn and per-role; the data card says apache-2.0 but the repository has no licence file and the scripts behind the data were not examined.","status":"active","body":{"text":"RoleLLM (arXiv 2310.00746v3, 18 Jun 2024) builds RoleBench: 95 English and 5 Chinese roles, 168,093 samples. Speaking style is scored as ROUGE-L overlap with GPT-4-generated reference answers (CUS), alongside GPT-4 and human win rates against a GPT-4 prompted baseline (RoleGPT). The human check used three NLP graduate students on a 500-instruction subset, with no agreement statistic in the text read. The setup is single-turn and per-role; the data card says apache-2.0 but the repository has no licence file and the scripts behind the data were not examined.\n\nCurator qualifications: RoleBench: style scored as ROUGE-L vs GPT-4 references; human check by three graduate students on 500 instructions, no agreement statistic.\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://huggingface.co/datasets/ZenMoore/RoleBench/raw/a57ed54f9613921e4a5f1b63601a558cd5acf971/README.md","note":"candidate evidence excerpts"},{"url":"https://raw.githubusercontent.com/InteractiveNLP-Team/RoleLLM-public/131a157c9962f46d36a29bcec6962b5acfa7644a/README.md","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":"RoleLLM (arXiv 2310.00746v3, 18 Jun 2024) builds RoleBench: 95 English and 5 Chinese roles, 168,093 samples. Speaking style is scored as ROUGE-L overlap with GPT-4-generated reference answers (CUS), alongside GPT-4 and human win rates against a GPT-4 prompted baseline (RoleGPT). The human check used three NLP graduate students on a 500-instruction subset, with no agreement statistic in the text read. 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Speaking style is scored as ROUGE-L overlap with GPT-4-generated reference answers (CUS), alongside GPT-4 and human win rates against a GPT-4 prompted baseline (RoleGPT). The human check used three NLP graduate students on a 500-instruction subset, with no agreement statistic in the text read. The setup is single-turn and per-role; the data card says apache-2.0 but the repository has no licence file and the scripts behind the data were not examined.\n\nCurator qualifications: RoleBench: style scored as ROUGE-L vs GPT-4 references; human check by three graduate students on 500 instructions, no agreement statistic.\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://huggingface.co/datasets/ZenMoore/RoleBench/raw/a57ed54f9613921e4a5f1b63601a558cd5acf971/README.md","note":"candidate evidence excerpts"},{"url":"https://raw.githubusercontent.com/InteractiveNLP-Team/RoleLLM-public/131a157c9962f46d36a29bcec6962b5acfa7644a/README.md","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"What exactly does RoleBench score for \"speaking style\", how were the human and GPT-4 judgements made, and what can its English data and licences be used for?","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":"RoleLLM (arXiv 2310.00746v3, 18 Jun 2024) builds RoleBench: 95 English and 5 Chinese roles, 168,093 samples. 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