{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:72CGD3B662QX3SPURKZDKWNEXB","short_pith_number":"pith:72CGD3B6","schema_version":"1.0","canonical_sha256":"fe8461ec3ef6a17dc9f48ab23559a4b86c73758ffc17b90c4a086c3fc0670ae9","source":{"kind":"arxiv","id":"2406.05587","version":1},"attestation_state":"computed","paper":{"title":"Creativity Has Left the Chat: The Price of Debiasing Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Behnam Mohammadi","submitted_at":"2024-06-08T22:14:51Z","abstract_excerpt":"Large Language Models (LLMs) have revolutionized natural language processing but can exhibit biases and may generate toxic content. While alignment techniques like Reinforcement Learning from Human Feedback (RLHF) reduce these issues, their impact on creativity, defined as syntactic and semantic diversity, remains unexplored. We investigate the unintended consequences of RLHF on the creativity of LLMs through three experiments focusing on the Llama-2 series. Our findings reveal that aligned models exhibit lower entropy in token predictions, form distinct clusters in the embedding space, and gr"},"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":"2406.05587","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-06-08T22:14:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b21fbb71350b2935d6d649a6667764e9e50edaba82e5ebebf4308a5e901bbccf","abstract_canon_sha256":"3fa76dc0f6890ffb7cbc9a0fb6659e75cba16bffe8e9627c2881b0e3da37034f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:29:30.333226Z","signature_b64":"v8i6YHWpZauqw3ywjLNt92Go/TuzP+MXyDzXdcirIXMXKmT5DRlCQne6eKYkkdzfd5eWDNhJEyvQn4qz00CxBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe8461ec3ef6a17dc9f48ab23559a4b86c73758ffc17b90c4a086c3fc0670ae9","last_reissued_at":"2026-07-05T08:29:30.332822Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:29:30.332822Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Creativity Has Left the Chat: The Price of Debiasing Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Behnam Mohammadi","submitted_at":"2024-06-08T22:14:51Z","abstract_excerpt":"Large Language Models (LLMs) have revolutionized natural language processing but can exhibit biases and may generate toxic content. While alignment techniques like Reinforcement Learning from Human Feedback (RLHF) reduce these issues, their impact on creativity, defined as syntactic and semantic diversity, remains unexplored. We investigate the unintended consequences of RLHF on the creativity of LLMs through three experiments focusing on the Llama-2 series. Our findings reveal that aligned models exhibit lower entropy in token predictions, form distinct clusters in the embedding space, and gr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05587","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/2406.05587/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":"2406.05587","created_at":"2026-07-05T08:29:30.332880+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.05587v1","created_at":"2026-07-05T08:29:30.332880+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05587","created_at":"2026-07-05T08:29:30.332880+00:00"},{"alias_kind":"pith_short_12","alias_value":"72CGD3B662QX","created_at":"2026-07-05T08:29:30.332880+00:00"},{"alias_kind":"pith_short_16","alias_value":"72CGD3B662QX3SPU","created_at":"2026-07-05T08:29:30.332880+00:00"},{"alias_kind":"pith_short_8","alias_value":"72CGD3B6","created_at":"2026-07-05T08:29:30.332880+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11762","citing_title":"Automated Creativity Evaluation of Language Models Across Open-Ended Tasks","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14746","citing_title":"Selective Safety Steering via Value-Filtered Decoding","ref_index":12,"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":53,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13776","citing_title":"\"Like Taking the Path of Least Resistance\": Exploring the Impact of LLM Interaction on the Creative Process of Programming","ref_index":49,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/72CGD3B662QX3SPURKZDKWNEXB","json":"https://pith.science/pith/72CGD3B662QX3SPURKZDKWNEXB.json","graph_json":"https://pith.science/api/pith-number/72CGD3B662QX3SPURKZDKWNEXB/graph.json","events_json":"https://pith.science/api/pith-number/72CGD3B662QX3SPURKZDKWNEXB/events.json","paper":"https://pith.science/paper/72CGD3B6"},"agent_actions":{"view_html":"https://pith.science/pith/72CGD3B662QX3SPURKZDKWNEXB","download_json":"https://pith.science/pith/72CGD3B662QX3SPURKZDKWNEXB.json","view_paper":"https://pith.science/paper/72CGD3B6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.05587&json=true","fetch_graph":"https://pith.science/api/pith-number/72CGD3B662QX3SPURKZDKWNEXB/graph.json","fetch_events":"https://pith.science/api/pith-number/72CGD3B662QX3SPURKZDKWNEXB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/72CGD3B662QX3SPURKZDKWNEXB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/72CGD3B662QX3SPURKZDKWNEXB/action/storage_attestation","attest_author":"https://pith.science/pith/72CGD3B662QX3SPURKZDKWNEXB/action/author_attestation","sign_citation":"https://pith.science/pith/72CGD3B662QX3SPURKZDKWNEXB/action/citation_signature","submit_replication":"https://pith.science/pith/72CGD3B662QX3SPURKZDKWNEXB/action/replication_record"}},"created_at":"2026-07-05T08:29:30.332880+00:00","updated_at":"2026-07-05T08:29:30.332880+00:00"}