{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZZAMHS3SZODGFJLKYWI2LESNM5","short_pith_number":"pith:ZZAMHS3S","schema_version":"1.0","canonical_sha256":"ce40c3cb72cb8662a56ac591a5924d674f0f44862823a62477a76315edf2a9e7","source":{"kind":"arxiv","id":"2301.06627","version":3},"attestation_state":"computed","paper":{"title":"Dissociating language and thought in large language models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Anna A. Ivanova, Evelina Fedorenko, Idan A. Blank, Joshua B. Tenenbaum, Kyle Mahowald, Nancy Kanwisher","submitted_at":"2023-01-16T22:41:19Z","abstract_excerpt":"Large Language Models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence -- knowledge of linguistic rules and patterns -- and functional linguistic competence -- understanding and using language in the world. We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their p"},"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":"2301.06627","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-01-16T22:41:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2ddd1c060938a42a0ea2a66a17713e32d8c29a4bd0b52fef3c60abbe14876a4a","abstract_canon_sha256":"bb1dfc9325b51fd0f3b62ae0c5005979d483c365d8a03b71f3e0b17b251b232c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:07:00.440701Z","signature_b64":"UnqF14AwcsjWQxiI9nGdnM2imFX2/99s7HEM2XzP4E+xf625s1MgN5DBf/qxVBalr+/muA/Eu/ShSQjZL5RZCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce40c3cb72cb8662a56ac591a5924d674f0f44862823a62477a76315edf2a9e7","last_reissued_at":"2026-07-05T08:07:00.440241Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:07:00.440241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dissociating language and thought in large language models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Anna A. Ivanova, Evelina Fedorenko, Idan A. Blank, Joshua B. Tenenbaum, Kyle Mahowald, Nancy Kanwisher","submitted_at":"2023-01-16T22:41:19Z","abstract_excerpt":"Large Language Models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction between formal linguistic competence -- knowledge of linguistic rules and patterns -- and functional linguistic competence -- understanding and using language in the world. We ground this distinction in human neuroscience, which has shown that formal and functional competence rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.06627","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/2301.06627/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":"2301.06627","created_at":"2026-07-05T08:07:00.440300+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.06627v3","created_at":"2026-07-05T08:07:00.440300+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.06627","created_at":"2026-07-05T08:07:00.440300+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZZAMHS3SZODG","created_at":"2026-07-05T08:07:00.440300+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZZAMHS3SZODGFJLK","created_at":"2026-07-05T08:07:00.440300+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZZAMHS3S","created_at":"2026-07-05T08:07:00.440300+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12040","citing_title":"A Lightweight Multi-Agent Framework for Automated Concrete Barrier Design","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30815","citing_title":"When transformers learn \"impossible\" languages, what do they learn?","ref_index":49,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20600","citing_title":"The New Associationism: Lessons from Deep Learning","ref_index":108,"is_internal_anchor":false},{"citing_arxiv_id":"2606.02211","citing_title":"Consistency Training while Mitigating Obfuscation via Rate Matching","ref_index":60,"is_internal_anchor":false},{"citing_arxiv_id":"2605.23035","citing_title":"Sparse Autoencoders Map Brain-LLM Alignment onto Cortical Semantic Topography","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2605.23039","citing_title":"Do Language Models Know What Not to Say? Causal Evidence for Statistical Preemption in LLMs","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.23054","citing_title":"Model Collapse as Cultural Evolution","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2310.17591","citing_title":"Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2309.16797","citing_title":"Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution","ref_index":220,"is_internal_anchor":false},{"citing_arxiv_id":"2304.11477","citing_title":"LLM+P: Empowering Large Language Models with Optimal Planning Proficiency","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17930","citing_title":"Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck?","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZZAMHS3SZODGFJLKYWI2LESNM5","json":"https://pith.science/pith/ZZAMHS3SZODGFJLKYWI2LESNM5.json","graph_json":"https://pith.science/api/pith-number/ZZAMHS3SZODGFJLKYWI2LESNM5/graph.json","events_json":"https://pith.science/api/pith-number/ZZAMHS3SZODGFJLKYWI2LESNM5/events.json","paper":"https://pith.science/paper/ZZAMHS3S"},"agent_actions":{"view_html":"https://pith.science/pith/ZZAMHS3SZODGFJLKYWI2LESNM5","download_json":"https://pith.science/pith/ZZAMHS3SZODGFJLKYWI2LESNM5.json","view_paper":"https://pith.science/paper/ZZAMHS3S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.06627&json=true","fetch_graph":"https://pith.science/api/pith-number/ZZAMHS3SZODGFJLKYWI2LESNM5/graph.json","fetch_events":"https://pith.science/api/pith-number/ZZAMHS3SZODGFJLKYWI2LESNM5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZZAMHS3SZODGFJLKYWI2LESNM5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZZAMHS3SZODGFJLKYWI2LESNM5/action/storage_attestation","attest_author":"https://pith.science/pith/ZZAMHS3SZODGFJLKYWI2LESNM5/action/author_attestation","sign_citation":"https://pith.science/pith/ZZAMHS3SZODGFJLKYWI2LESNM5/action/citation_signature","submit_replication":"https://pith.science/pith/ZZAMHS3SZODGFJLKYWI2LESNM5/action/replication_record"}},"created_at":"2026-07-05T08:07:00.440300+00:00","updated_at":"2026-07-05T08:07:00.440300+00:00"}