{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4I2DPAKDDYOPENQ7E3CEPJ7GEH","short_pith_number":"pith:4I2DPAKD","schema_version":"1.0","canonical_sha256":"e2343781431e1cf2361f26c447a7e621ff02ee7d10356b0d439f2b1b6edb31f8","source":{"kind":"arxiv","id":"2405.20331","version":2},"attestation_state":"computed","paper":{"title":"CoSy: Evaluating Textual Explanations of Neurons","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Anna Hedstr\\\"om, Kirill Bykov, Laura Kopf, Marina M.-C. H\\\"ohne, Philine Lou Bommer, Sebastian Lapuschkin","submitted_at":"2024-05-30T17:59:04Z","abstract_excerpt":"A crucial aspect of understanding the complex nature of Deep Neural Networks (DNNs) is the ability to explain learned concepts within their latent representations. While methods exist to connect neurons to human-understandable textual descriptions, evaluating the quality of these explanations is challenging due to the lack of a unified quantitative approach. We introduce CoSy (Concept Synthesis), a novel, architecture-agnostic framework for evaluating textual explanations of latent neurons. Given textual explanations, our proposed framework uses a generative model conditioned on textual input "},"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":"2405.20331","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-30T17:59:04Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"439b2d6a7ce5391f83f7af7f6eb45de6894f2a5f21a4d403fae48bc92ebd2493","abstract_canon_sha256":"84d825e8e4a5f4a4f79adc3ca836f6d72b136efb5c66bef71f908bf606fc7325"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:44:50.964526Z","signature_b64":"8NSRJVmRAo+Ygg6iw8UUF9zAKCRxvA/qJVoNkhge2Pi9YARBdQdPKtcwPGZmXzIrQtuWNUiFBL+sx7X+m2nKBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2343781431e1cf2361f26c447a7e621ff02ee7d10356b0d439f2b1b6edb31f8","last_reissued_at":"2026-07-05T09:44:50.964012Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:44:50.964012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CoSy: Evaluating Textual Explanations of Neurons","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Anna Hedstr\\\"om, Kirill Bykov, Laura Kopf, Marina M.-C. H\\\"ohne, Philine Lou Bommer, Sebastian Lapuschkin","submitted_at":"2024-05-30T17:59:04Z","abstract_excerpt":"A crucial aspect of understanding the complex nature of Deep Neural Networks (DNNs) is the ability to explain learned concepts within their latent representations. While methods exist to connect neurons to human-understandable textual descriptions, evaluating the quality of these explanations is challenging due to the lack of a unified quantitative approach. We introduce CoSy (Concept Synthesis), a novel, architecture-agnostic framework for evaluating textual explanations of latent neurons. Given textual explanations, our proposed framework uses a generative model conditioned on textual input "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.20331","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/2405.20331/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":"2405.20331","created_at":"2026-07-05T09:44:50.964081+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.20331v2","created_at":"2026-07-05T09:44:50.964081+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.20331","created_at":"2026-07-05T09:44:50.964081+00:00"},{"alias_kind":"pith_short_12","alias_value":"4I2DPAKDDYOP","created_at":"2026-07-05T09:44:50.964081+00:00"},{"alias_kind":"pith_short_16","alias_value":"4I2DPAKDDYOPENQ7","created_at":"2026-07-05T09:44:50.964081+00:00"},{"alias_kind":"pith_short_8","alias_value":"4I2DPAKD","created_at":"2026-07-05T09:44:50.964081+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.05774","citing_title":"Evaluating Neuron Explanations: A Unified Framework with Sanity Checks","ref_index":30,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4I2DPAKDDYOPENQ7E3CEPJ7GEH","json":"https://pith.science/pith/4I2DPAKDDYOPENQ7E3CEPJ7GEH.json","graph_json":"https://pith.science/api/pith-number/4I2DPAKDDYOPENQ7E3CEPJ7GEH/graph.json","events_json":"https://pith.science/api/pith-number/4I2DPAKDDYOPENQ7E3CEPJ7GEH/events.json","paper":"https://pith.science/paper/4I2DPAKD"},"agent_actions":{"view_html":"https://pith.science/pith/4I2DPAKDDYOPENQ7E3CEPJ7GEH","download_json":"https://pith.science/pith/4I2DPAKDDYOPENQ7E3CEPJ7GEH.json","view_paper":"https://pith.science/paper/4I2DPAKD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.20331&json=true","fetch_graph":"https://pith.science/api/pith-number/4I2DPAKDDYOPENQ7E3CEPJ7GEH/graph.json","fetch_events":"https://pith.science/api/pith-number/4I2DPAKDDYOPENQ7E3CEPJ7GEH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4I2DPAKDDYOPENQ7E3CEPJ7GEH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4I2DPAKDDYOPENQ7E3CEPJ7GEH/action/storage_attestation","attest_author":"https://pith.science/pith/4I2DPAKDDYOPENQ7E3CEPJ7GEH/action/author_attestation","sign_citation":"https://pith.science/pith/4I2DPAKDDYOPENQ7E3CEPJ7GEH/action/citation_signature","submit_replication":"https://pith.science/pith/4I2DPAKDDYOPENQ7E3CEPJ7GEH/action/replication_record"}},"created_at":"2026-07-05T09:44:50.964081+00:00","updated_at":"2026-07-05T09:44:50.964081+00:00"}