{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DXDGZHIF57WLPA4CMMQUAE5AOU","short_pith_number":"pith:DXDGZHIF","schema_version":"1.0","canonical_sha256":"1dc66c9d05efecb7838263214013a0752f091f6ce53a8e5cca303b03d276d944","source":{"kind":"arxiv","id":"2309.14556","version":3},"attestation_state":"computed","paper":{"title":"Art or Artifice? Large Language Models and the False Promise of Creativity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Chien-Sheng Wu, Divyansh Agarwal, Philippe Laban, Smaranda Muresan, Tuhin Chakrabarty","submitted_at":"2023-09-25T22:02:46Z","abstract_excerpt":"Researchers have argued that large language models (LLMs) exhibit high-quality writing capabilities from blogs to stories. However, evaluating objectively the creativity of a piece of writing is challenging. Inspired by the Torrance Test of Creative Thinking (TTCT), which measures creativity as a process, we use the Consensual Assessment Technique [3] and propose the Torrance Test of Creative Writing (TTCW) to evaluate creativity as a product. TTCW consists of 14 binary tests organized into the original dimensions of Fluency, Flexibility, Originality, and Elaboration. We recruit 10 creative wr"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2309.14556","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-09-25T22:02:46Z","cross_cats_sorted":["cs.AI","cs.HC"],"title_canon_sha256":"195960e5cdd217930ab8d4b575d687d6f0867f023ae478b105fc88457471994f","abstract_canon_sha256":"90ad2d746f04a3947d3146d63e6384909fb8b97a8e26cdebe273fa7216d8a344"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:53:31.177595Z","signature_b64":"gDfWQ03hdRSopNjunnfKF5KsmThFc4mb75Z0X3GEhp6GVySUsTnO7bfebyoHKckF+uNOGiyq9yjSnSDvyUJUDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1dc66c9d05efecb7838263214013a0752f091f6ce53a8e5cca303b03d276d944","last_reissued_at":"2026-07-05T07:53:31.177139Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:53:31.177139Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Art or Artifice? Large Language Models and the False Promise of Creativity","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CL","authors_text":"Chien-Sheng Wu, Divyansh Agarwal, Philippe Laban, Smaranda Muresan, Tuhin Chakrabarty","submitted_at":"2023-09-25T22:02:46Z","abstract_excerpt":"Researchers have argued that large language models (LLMs) exhibit high-quality writing capabilities from blogs to stories. However, evaluating objectively the creativity of a piece of writing is challenging. Inspired by the Torrance Test of Creative Thinking (TTCT), which measures creativity as a process, we use the Consensual Assessment Technique [3] and propose the Torrance Test of Creative Writing (TTCW) to evaluate creativity as a product. TTCW consists of 14 binary tests organized into the original dimensions of Fluency, Flexibility, Originality, and Elaboration. We recruit 10 creative wr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.14556","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2309.14556/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2309.14556","created_at":"2026-07-05T07:53:31.177199+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.14556v3","created_at":"2026-07-05T07:53:31.177199+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.14556","created_at":"2026-07-05T07:53:31.177199+00:00"},{"alias_kind":"pith_short_12","alias_value":"DXDGZHIF57WL","created_at":"2026-07-05T07:53:31.177199+00:00"},{"alias_kind":"pith_short_16","alias_value":"DXDGZHIF57WLPA4C","created_at":"2026-07-05T07:53:31.177199+00:00"},{"alias_kind":"pith_short_8","alias_value":"DXDGZHIF","created_at":"2026-07-05T07:53:31.177199+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09843","citing_title":"An LLM-Native Psychometric Instrument Reveals a Self-Report--Behavior Gap Across 25 Models","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2606.01451","citing_title":"Before and After Temperature: A Distributional View of Creative LLM Generation","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04831","citing_title":"StoryAlign: Evaluating and Training Reward Models for Story Generation","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DXDGZHIF57WLPA4CMMQUAE5AOU","json":"https://pith.science/pith/DXDGZHIF57WLPA4CMMQUAE5AOU.json","graph_json":"https://pith.science/api/pith-number/DXDGZHIF57WLPA4CMMQUAE5AOU/graph.json","events_json":"https://pith.science/api/pith-number/DXDGZHIF57WLPA4CMMQUAE5AOU/events.json","paper":"https://pith.science/paper/DXDGZHIF"},"agent_actions":{"view_html":"https://pith.science/pith/DXDGZHIF57WLPA4CMMQUAE5AOU","download_json":"https://pith.science/pith/DXDGZHIF57WLPA4CMMQUAE5AOU.json","view_paper":"https://pith.science/paper/DXDGZHIF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.14556&json=true","fetch_graph":"https://pith.science/api/pith-number/DXDGZHIF57WLPA4CMMQUAE5AOU/graph.json","fetch_events":"https://pith.science/api/pith-number/DXDGZHIF57WLPA4CMMQUAE5AOU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DXDGZHIF57WLPA4CMMQUAE5AOU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DXDGZHIF57WLPA4CMMQUAE5AOU/action/storage_attestation","attest_author":"https://pith.science/pith/DXDGZHIF57WLPA4CMMQUAE5AOU/action/author_attestation","sign_citation":"https://pith.science/pith/DXDGZHIF57WLPA4CMMQUAE5AOU/action/citation_signature","submit_replication":"https://pith.science/pith/DXDGZHIF57WLPA4CMMQUAE5AOU/action/replication_record"}},"created_at":"2026-07-05T07:53:31.177199+00:00","updated_at":"2026-07-05T07:53:31.177199+00:00"}