{"schema":"record/v1","id":"art_01M5MB3F8H2541C0Q1W64Z5HTM","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Can an LLM serve as a judge of story or creative-writing quality, and how reliable and biased is it?","summary":"Two 2023 papers disagree. Chiang and Lee (text-davinci-003, ChatGPT) rated 200 human and 200 GPT-2 stories on four attributes and ranked human stories higher like English teachers, but per-story Kendall tau with teachers was only 0.12-0.43 and T0/text-curie-001 did not separate them. Chakrabarty et al. had GPT-3.5, GPT-4 and Claude v1.3 administer 14 creativity tests on 48 longer stories and found Cohen kappa with expert majority near zero (averages 0.016, 0.035, -0.006). Reported failure modes: score-level shifts between LLMs, refusal or low likability for violent content, sampling instability in small models; length bias is controlled by Chakrabarty but not measured in either paper.","status":"active","body":{"text":"Two 2023 papers disagree. Chiang and Lee (text-davinci-003, ChatGPT) rated 200 human and 200 GPT-2 stories on four attributes and ranked human stories higher like English teachers, but per-story Kendall tau with teachers was only 0.12-0.43 and T0/text-curie-001 did not separate them. Chakrabarty et al. had GPT-3.5, GPT-4 and Claude v1.3 administer 14 creativity tests on 48 longer stories and found Cohen kappa with expert majority near zero (averages 0.016, 0.035, -0.006). Reported failure modes: score-level shifts between LLMs, refusal or low likability for violent content, sampling instability in small models; length bias is controlled by Chakrabarty but not measured in either paper.\n\nCurator qualifications: Chiang & Lee: '0.12' 3, '0.43' 3, 'text-davinci-003' 27 hits. Chakrabarty et al.: '0.016' 1, '0.035' 1, '-0.006' 1; '48 stories' 8 hits.\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/2305.01937v1","note":"Chiang & Lee arXiv 2305.01937v1 PDF; Chakrabarty et al. arXiv 2309.14556v3 PDF"},{"url":"https://arxiv.org/abs/2309.14556v3","note":"Chiang & Lee arXiv 2305.01937v1 PDF; Chakrabarty et al. arXiv 2309.14556v3 PDF"}]},"categories":["T01","T09","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/2305.01937v1","claim":"Two 2023 papers disagree. 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Reported failure modes: score-level shifts between LLMs, refusal or low likability for violent content, sampling instability in small models; length bias is controlled by Chakrabarty but not measured in either paper.","locator":"Chiang & Lee arXiv 2305.01937v1 PDF; Chakrabarty et al. arXiv 2309.14556v3 PDF","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-26991bf0753f5785955ce0ca2cb8b03a9197795e/record.json#p-26991bf0753f5785955ce0ca2cb8b03a9197795e/record","import_batch_id":"pipe-546dc2dd6dbd03046d392f701c7b963d99e54c51","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-546dc2dd6dbd03046d392f701c7b963d99e54c51","original":{"body":{"text":"Two 2023 papers disagree. Chiang and Lee (text-davinci-003, ChatGPT) rated 200 human and 200 GPT-2 stories on four attributes and ranked human stories higher like English teachers, but per-story Kendall tau with teachers was only 0.12-0.43 and T0/text-curie-001 did not separate them. Chakrabarty et al. had GPT-3.5, GPT-4 and Claude v1.3 administer 14 creativity tests on 48 longer stories and found Cohen kappa with expert majority near zero (averages 0.016, 0.035, -0.006). Reported failure modes: score-level shifts between LLMs, refusal or low likability for violent content, sampling instability in small models; length bias is controlled by Chakrabarty but not measured in either paper.\n\nCurator qualifications: Chiang & Lee: '0.12' 3, '0.43' 3, 'text-davinci-003' 27 hits. Chakrabarty et al.: '0.016' 1, '0.035' 1, '-0.006' 1; '48 stories' 8 hits.\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/2305.01937v1","note":"Chiang & Lee arXiv 2305.01937v1 PDF; Chakrabarty et al. arXiv 2309.14556v3 PDF"},{"url":"https://arxiv.org/abs/2309.14556v3","note":"Chiang & Lee arXiv 2305.01937v1 PDF; Chakrabarty et al. arXiv 2309.14556v3 PDF"}]},"tags":[],"type":"article","links":[],"title":"Can an LLM serve as a judge of story or creative-writing quality, and how reliable and biased is it?","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":"Two 2023 papers disagree. Chiang and Lee (text-davinci-003, ChatGPT) rated 200 human and 200 GPT-2 stories on four attributes and ranked human stories higher like English teachers, but per-story Kendall tau with teachers was only 0.12-0.43 and T0/text-curie-001 did not separate them. Chakrabarty et al. had GPT-3.5, GPT-4 and Claude v1.3 administer 14 creativity tests on 48 longer stories and found Cohen kappa with expert majority near zero (averages 0.016, 0.035, -0.006). Reported failure modes: score-level shifts between LLMs, refusal or low likability for violent content, sampling instability in small models; length bias is controlled by Chakrabarty but not measured in either paper.","evidence":{"claims":[{"url":"https://arxiv.org/abs/2305.01937v1","claim":"Two 2023 papers disagree. Chiang and Lee (text-davinci-003, ChatGPT) rated 200 human and 200 GPT-2 stories on four attributes and ranked human stories higher like English teachers, but per-story Kendall tau with teachers was only 0.12-0.43 and T0/text-curie-001 did not separate them. Chakrabarty et al. had GPT-3.5, GPT-4 and Claude v1.3 administer 14 creativity tests on 48 longer stories and found Cohen kappa with expert majority near zero (averages 0.016, 0.035, -0.006). Reported failure modes: score-level shifts between LLMs, refusal or low likability for violent content, sampling instability in small models; length bias is controlled by Chakrabarty but not measured in either paper.","locator":"Chiang & Lee arXiv 2305.01937v1 PDF; Chakrabarty et al. arXiv 2309.14556v3 PDF","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"source_inspected","execution_record_ids":[]},"language":"en","categories":["T01","T09","T20"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":3,"topic_ids":["CAT:T01.01.a","CAT:T01.01.b","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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