{"schema":"record/v1","id":"art_01M5MC7TBWJ0253MB0RG2D7V20","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Did the ECHO paper have people rate LLM-generated role-play answers against a real person, how many raters and items, what were the results, and were its LLM judges checked against the humans?","summary":"ECHO (arXiv 2404.13957v1, 22 Apr 2024) does include a human evaluation of LLM-generated text: acquaintances of ten real volunteers (at least seven each) saw ten pairs of answers, one from the real person and one from a role-playing LLM, and picked the human one. The headline metric is how often the LLM answer was chosen (50% is chance). Overall rates ran 37.5 to 48.2 across methods, with GPTs closest to chance. The targets are ordinary living people, not fictional characters, and the task is human-versus-machine, not which character said a line, so S52's negative narrows rather than falls. No agreement statistic was found, and LLM judges were scored against ground truth, not validated against the human raters; they picked the LLM answer as the human one above 90% for the two GPT-4 judges.","status":"active","body":{"text":"ECHO (arXiv 2404.13957v1, 22 Apr 2024) does include a human evaluation of LLM-generated text: acquaintances of ten real volunteers (at least seven each) saw ten pairs of answers, one from the real person and one from a role-playing LLM, and picked the human one. The headline metric is how often the LLM answer was chosen (50% is chance). Overall rates ran 37.5 to 48.2 across methods, with GPTs closest to chance. The targets are ordinary living people, not fictional characters, and the task is human-versus-machine, not which character said a line, so S52's negative narrows rather than falls. No agreement statistic was found, and LLM judges were scored against ground truth, not validated against the human raters; they picked the LLM answer as the human one above 90% for the two GPT-4 judges.\n\nCurator qualifications: ECHO v1: acquaintances of ten real volunteers chose between a real and an LLM answer; LLM chosen 37.5-48.2% by method (50% chance). Tests imitation of real people, not fictional voice; internal number mismatches (48.3 vs 48.2, 80 vs 83.3); 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/abs/2404.13957","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2404.13957v1","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/abs/2404.13957","claim":"ECHO (arXiv 2404.13957v1, 22 Apr 2024) does include a human evaluation of LLM-generated text: acquaintances of ten real volunteers (at least seven each) saw ten pairs of answers, one from the real person and one from a role-playing LLM, and picked the human one. The headline metric is how often the LLM answer was chosen (50% is chance). Overall rates ran 37.5 to 48.2 across methods, with GPTs closest to chance. The targets are ordinary living people, not fictional characters, and the task is human-versus-machine, not which character said a line, so S52's negative narrows rather than falls. No agreement statistic was found, and LLM judges were scored against ground truth, not validated against the human raters; they picked the LLM answer as the human one above 90% for the two GPT-4 judges.","locator":"candidate evidence excerpts","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-fab985856f943c9dfc18cefbce61b0d1a4e8eece/record.json#p-fab985856f943c9dfc18cefbce61b0d1a4e8eece/record","import_batch_id":"pipe-937a1dcc112e525a9a0a946277a36b9c0feed3fb","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-937a1dcc112e525a9a0a946277a36b9c0feed3fb","original":{"body":{"text":"ECHO (arXiv 2404.13957v1, 22 Apr 2024) does include a human evaluation of LLM-generated text: acquaintances of ten real volunteers (at least seven each) saw ten pairs of answers, one from the real person and one from a role-playing LLM, and picked the human one. The headline metric is how often the LLM answer was chosen (50% is chance). Overall rates ran 37.5 to 48.2 across methods, with GPTs closest to chance. The targets are ordinary living people, not fictional characters, and the task is human-versus-machine, not which character said a line, so S52's negative narrows rather than falls. No agreement statistic was found, and LLM judges were scored against ground truth, not validated against the human raters; they picked the LLM answer as the human one above 90% for the two GPT-4 judges.\n\nCurator qualifications: ECHO v1: acquaintances of ten real volunteers chose between a real and an LLM answer; LLM chosen 37.5-48.2% by method (50% chance). Tests imitation of real people, not fictional voice; internal number mismatches (48.3 vs 48.2, 80 vs 83.3); 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/abs/2404.13957","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2404.13957v1","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"Did the ECHO paper have people rate LLM-generated role-play answers against a real person, how many raters and items, what were the results, and were its LLM judges checked against the humans?","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":"ECHO (arXiv 2404.13957v1, 22 Apr 2024) does include a human evaluation of LLM-generated text: acquaintances of ten real volunteers (at least seven each) saw ten pairs of answers, one from the real person and one from a role-playing LLM, and picked the human one. The headline metric is how often the LLM answer was chosen (50% is chance). Overall rates ran 37.5 to 48.2 across methods, with GPTs closest to chance. The targets are ordinary living people, not fictional characters, and the task is human-versus-machine, not which character said a line, so S52's negative narrows rather than falls. No agreement statistic was found, and LLM judges were scored against ground truth, not validated against the human raters; they picked the LLM answer as the human one above 90% for the two GPT-4 judges.","evidence":{"claims":[{"url":"https://arxiv.org/abs/2404.13957","claim":"ECHO (arXiv 2404.13957v1, 22 Apr 2024) does include a human evaluation of LLM-generated text: acquaintances of ten real volunteers (at least seven each) saw ten pairs of answers, one from the real person and one from a role-playing LLM, and picked the human one. The headline metric is how often the LLM answer was chosen (50% is chance). Overall rates ran 37.5 to 48.2 across methods, with GPTs closest to chance. The targets are ordinary living people, not fictional characters, and the task is human-versus-machine, not which character said a line, so S52's negative narrows rather than falls. No agreement statistic was found, and LLM judges were scored against ground truth, not validated against the human raters; they picked the LLM answer as the human one above 90% for the two GPT-4 judges.","locator":"candidate evidence excerpts","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:T06.AIC.13"],"confidence":"Not stated; see limitations.","limitations":["Source pin hashes are native catalogue descriptor hashes, not upstream byte hashes. 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