{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:QSE2QIKBSZTSZHZZZOITRM5U7R","short_pith_number":"pith:QSE2QIKB","schema_version":"1.0","canonical_sha256":"8489a8214196672c9f39cb9138b3b4fc5fb83d2d10bdef1bf0d37be34377a236","source":{"kind":"arxiv","id":"2206.08932","version":1},"attestation_state":"computed","paper":{"title":"Putting GPT-3's Creativity to the (Alternative Uses) Test","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.HC"],"primary_cat":"cs.AI","authors_text":"Claire Stevenson, Han van der Maas, Iris Smal, Matthijs Baas, Raoul Grasman","submitted_at":"2022-06-10T15:36:45Z","abstract_excerpt":"AI large language models have (co-)produced amazing written works from newspaper articles to novels and poetry. These works meet the standards of the standard definition of creativity: being original and useful, and sometimes even the additional element of surprise. But can a large language model designed to predict the next text fragment provide creative, out-of-the-box, responses that still solve the problem at hand? We put Open AI's generative natural language model, GPT-3, to the test. Can it provide creative solutions to one of the most commonly used tests in creativity research? We asses"},"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":"2206.08932","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2022-06-10T15:36:45Z","cross_cats_sorted":["cs.CL","cs.HC"],"title_canon_sha256":"36b6b3c8d57a4c85cf6440e24980a5e76f4818694663a33cdb7734659922ece7","abstract_canon_sha256":"4514d7152481c9d609c4bc007ec7d9c309b7a64318cfa03844c742180fba7b62"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:33:01.738115Z","signature_b64":"OwXhfsYSh712aOZdM8eqOEfk+2PG1ndPS6Ze1Kp/LkI7uACPXZKwY41hDVeoKZBlYwXtbKB/hKYbHYikhRwYDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8489a8214196672c9f39cb9138b3b4fc5fb83d2d10bdef1bf0d37be34377a236","last_reissued_at":"2026-07-05T04:33:01.727395Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:33:01.727395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Putting GPT-3's Creativity to the (Alternative Uses) Test","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.HC"],"primary_cat":"cs.AI","authors_text":"Claire Stevenson, Han van der Maas, Iris Smal, Matthijs Baas, Raoul Grasman","submitted_at":"2022-06-10T15:36:45Z","abstract_excerpt":"AI large language models have (co-)produced amazing written works from newspaper articles to novels and poetry. These works meet the standards of the standard definition of creativity: being original and useful, and sometimes even the additional element of surprise. But can a large language model designed to predict the next text fragment provide creative, out-of-the-box, responses that still solve the problem at hand? We put Open AI's generative natural language model, GPT-3, to the test. Can it provide creative solutions to one of the most commonly used tests in creativity research? We asses"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.08932","kind":"arxiv","version":1},"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/2206.08932/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":"2206.08932","created_at":"2026-07-05T04:33:01.727654+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.08932v1","created_at":"2026-07-05T04:33:01.727654+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.08932","created_at":"2026-07-05T04:33:01.727654+00:00"},{"alias_kind":"pith_short_12","alias_value":"QSE2QIKBSZTS","created_at":"2026-07-05T04:33:01.727654+00:00"},{"alias_kind":"pith_short_16","alias_value":"QSE2QIKBSZTSZHZZ","created_at":"2026-07-05T04:33:01.727654+00:00"},{"alias_kind":"pith_short_8","alias_value":"QSE2QIKB","created_at":"2026-07-05T04:33:01.727654+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07522","citing_title":"Two-player Alternate Uses Test: A Controlled Testbed for Interactive Human-AI and Human-Human Co-Creation","ref_index":25,"is_internal_anchor":true},{"citing_arxiv_id":"2606.01451","citing_title":"Before and After Temperature: A Distributional View of Creative LLM Generation","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2606.10113","citing_title":"Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editing","ref_index":175,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17885","citing_title":"Multi-agent AI systems outperform human teams in creativity","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03480","citing_title":"Large Language Models Align with the Human Brain during Creative Thinking","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QSE2QIKBSZTSZHZZZOITRM5U7R","json":"https://pith.science/pith/QSE2QIKBSZTSZHZZZOITRM5U7R.json","graph_json":"https://pith.science/api/pith-number/QSE2QIKBSZTSZHZZZOITRM5U7R/graph.json","events_json":"https://pith.science/api/pith-number/QSE2QIKBSZTSZHZZZOITRM5U7R/events.json","paper":"https://pith.science/paper/QSE2QIKB"},"agent_actions":{"view_html":"https://pith.science/pith/QSE2QIKBSZTSZHZZZOITRM5U7R","download_json":"https://pith.science/pith/QSE2QIKBSZTSZHZZZOITRM5U7R.json","view_paper":"https://pith.science/paper/QSE2QIKB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.08932&json=true","fetch_graph":"https://pith.science/api/pith-number/QSE2QIKBSZTSZHZZZOITRM5U7R/graph.json","fetch_events":"https://pith.science/api/pith-number/QSE2QIKBSZTSZHZZZOITRM5U7R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QSE2QIKBSZTSZHZZZOITRM5U7R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QSE2QIKBSZTSZHZZZOITRM5U7R/action/storage_attestation","attest_author":"https://pith.science/pith/QSE2QIKBSZTSZHZZZOITRM5U7R/action/author_attestation","sign_citation":"https://pith.science/pith/QSE2QIKBSZTSZHZZZOITRM5U7R/action/citation_signature","submit_replication":"https://pith.science/pith/QSE2QIKBSZTSZHZZZOITRM5U7R/action/replication_record"}},"created_at":"2026-07-05T04:33:01.727654+00:00","updated_at":"2026-07-05T04:33:01.727654+00:00"}