{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JH53OGFK33YB4EUXTOQ2IKVGAS","short_pith_number":"pith:JH53OGFK","schema_version":"1.0","canonical_sha256":"49fbb718aadef01e12979ba1a42aa6049d56a8c75d482db4266a0b5911ef6eaa","source":{"kind":"arxiv","id":"2305.13386","version":2},"attestation_state":"computed","paper":{"title":"Can LLMs facilitate interpretation of pre-trained language models?","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Basel Mousi, Fahim Dalvi, Nadir Durrani","submitted_at":"2023-05-22T18:03:13Z","abstract_excerpt":"Work done to uncover the knowledge encoded within pre-trained language models rely on annotated corpora or human-in-the-loop methods. However, these approaches are limited in terms of scalability and the scope of interpretation. We propose using a large language model, ChatGPT, as an annotator to enable fine-grained interpretation analysis of pre-trained language models. We discover latent concepts within pre-trained language models by applying agglomerative hierarchical clustering over contextualized representations and then annotate these concepts using ChatGPT. Our findings demonstrate that"},"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":"2305.13386","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-22T18:03:13Z","cross_cats_sorted":[],"title_canon_sha256":"e2239ebe280548af5ca2345bad9b9382756b4750632b4efbbb8d99cb7d306222","abstract_canon_sha256":"aa9c897e83560b321e27e2e978a23c74d73728b445f70bbc7468a746257e4b86"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:02:50.370180Z","signature_b64":"Z43SG8Fk0A9dGPMzQzR5CKVURrXK858DeD0PQZOTPI16RxDb8DrgHeoUnOe4W75hP2juFzA9jo+47fLDYiXwCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49fbb718aadef01e12979ba1a42aa6049d56a8c75d482db4266a0b5911ef6eaa","last_reissued_at":"2026-07-05T07:02:50.369638Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:02:50.369638Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can LLMs facilitate interpretation of pre-trained language models?","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Basel Mousi, Fahim Dalvi, Nadir Durrani","submitted_at":"2023-05-22T18:03:13Z","abstract_excerpt":"Work done to uncover the knowledge encoded within pre-trained language models rely on annotated corpora or human-in-the-loop methods. However, these approaches are limited in terms of scalability and the scope of interpretation. We propose using a large language model, ChatGPT, as an annotator to enable fine-grained interpretation analysis of pre-trained language models. We discover latent concepts within pre-trained language models by applying agglomerative hierarchical clustering over contextualized representations and then annotate these concepts using ChatGPT. Our findings demonstrate that"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.13386","kind":"arxiv","version":2},"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/2305.13386/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":"2305.13386","created_at":"2026-07-05T07:02:50.369700+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.13386v2","created_at":"2026-07-05T07:02:50.369700+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.13386","created_at":"2026-07-05T07:02:50.369700+00:00"},{"alias_kind":"pith_short_12","alias_value":"JH53OGFK33YB","created_at":"2026-07-05T07:02:50.369700+00:00"},{"alias_kind":"pith_short_16","alias_value":"JH53OGFK33YB4EUX","created_at":"2026-07-05T07:02:50.369700+00:00"},{"alias_kind":"pith_short_8","alias_value":"JH53OGFK","created_at":"2026-07-05T07:02:50.369700+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.15710","citing_title":"The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition","ref_index":86,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JH53OGFK33YB4EUXTOQ2IKVGAS","json":"https://pith.science/pith/JH53OGFK33YB4EUXTOQ2IKVGAS.json","graph_json":"https://pith.science/api/pith-number/JH53OGFK33YB4EUXTOQ2IKVGAS/graph.json","events_json":"https://pith.science/api/pith-number/JH53OGFK33YB4EUXTOQ2IKVGAS/events.json","paper":"https://pith.science/paper/JH53OGFK"},"agent_actions":{"view_html":"https://pith.science/pith/JH53OGFK33YB4EUXTOQ2IKVGAS","download_json":"https://pith.science/pith/JH53OGFK33YB4EUXTOQ2IKVGAS.json","view_paper":"https://pith.science/paper/JH53OGFK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.13386&json=true","fetch_graph":"https://pith.science/api/pith-number/JH53OGFK33YB4EUXTOQ2IKVGAS/graph.json","fetch_events":"https://pith.science/api/pith-number/JH53OGFK33YB4EUXTOQ2IKVGAS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JH53OGFK33YB4EUXTOQ2IKVGAS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JH53OGFK33YB4EUXTOQ2IKVGAS/action/storage_attestation","attest_author":"https://pith.science/pith/JH53OGFK33YB4EUXTOQ2IKVGAS/action/author_attestation","sign_citation":"https://pith.science/pith/JH53OGFK33YB4EUXTOQ2IKVGAS/action/citation_signature","submit_replication":"https://pith.science/pith/JH53OGFK33YB4EUXTOQ2IKVGAS/action/replication_record"}},"created_at":"2026-07-05T07:02:50.369700+00:00","updated_at":"2026-07-05T07:02:50.369700+00:00"}