{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:757IGEGNERM2DBVG2J3ONJFDPC","short_pith_number":"pith:757IGEGN","schema_version":"1.0","canonical_sha256":"ff7e8310cd2459a186a6d276e6a4a37898674b71d0be59ba7980ff1bdea91e48","source":{"kind":"arxiv","id":"2403.17299","version":1},"attestation_state":"computed","paper":{"title":"Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.NC"],"primary_cat":"cs.CL","authors_text":"Ercong Nie, Jonathan R. Brennan, Linyang He, Peili Chen, Yuanning Li","submitted_at":"2024-03-26T00:56:06Z","abstract_excerpt":"Inspired by cognitive neuroscience studies, we introduce a novel `decoding probing' method that uses minimal pairs benchmark (BLiMP) to probe internal linguistic characteristics in neural language models layer by layer. By treating the language model as the `brain' and its representations as `neural activations', we decode grammaticality labels of minimal pairs from the intermediate layers' representations. This approach reveals: 1) Self-supervised language models capture abstract linguistic structures in intermediate layers that GloVe and RNN language models cannot learn. 2) Information about"},"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":"2403.17299","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-26T00:56:06Z","cross_cats_sorted":["q-bio.NC"],"title_canon_sha256":"751a02de8a66ff8d05d8c6ed62b86f9ca2722b17c4a173f0d082dd1330688832","abstract_canon_sha256":"ad95068bdfa956afe0c6f50988ff202ef682856f2a13992389796ac73ed599b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:00:48.541624Z","signature_b64":"YcEGZWpH3XBDfDDCS6e4DNRwdP+VNRTn0Yo8OMVN9HN2CMt4OqpN4C+nnCa3rFhKbVk3PlZS0Bns4dUZf7hIBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff7e8310cd2459a186a6d276e6a4a37898674b71d0be59ba7980ff1bdea91e48","last_reissued_at":"2026-07-05T08:00:48.541170Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:00:48.541170Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.NC"],"primary_cat":"cs.CL","authors_text":"Ercong Nie, Jonathan R. Brennan, Linyang He, Peili Chen, Yuanning Li","submitted_at":"2024-03-26T00:56:06Z","abstract_excerpt":"Inspired by cognitive neuroscience studies, we introduce a novel `decoding probing' method that uses minimal pairs benchmark (BLiMP) to probe internal linguistic characteristics in neural language models layer by layer. By treating the language model as the `brain' and its representations as `neural activations', we decode grammaticality labels of minimal pairs from the intermediate layers' representations. This approach reveals: 1) Self-supervised language models capture abstract linguistic structures in intermediate layers that GloVe and RNN language models cannot learn. 2) Information about"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.17299","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/2403.17299/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":"2403.17299","created_at":"2026-07-05T08:00:48.541231+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.17299v1","created_at":"2026-07-05T08:00:48.541231+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.17299","created_at":"2026-07-05T08:00:48.541231+00:00"},{"alias_kind":"pith_short_12","alias_value":"757IGEGNERM2","created_at":"2026-07-05T08:00:48.541231+00:00"},{"alias_kind":"pith_short_16","alias_value":"757IGEGNERM2DBVG","created_at":"2026-07-05T08:00:48.541231+00:00"},{"alias_kind":"pith_short_8","alias_value":"757IGEGN","created_at":"2026-07-05T08:00:48.541231+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.19260","citing_title":"Understanding the Mechanism of Altruism in Large Language Models","ref_index":275,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/757IGEGNERM2DBVG2J3ONJFDPC","json":"https://pith.science/pith/757IGEGNERM2DBVG2J3ONJFDPC.json","graph_json":"https://pith.science/api/pith-number/757IGEGNERM2DBVG2J3ONJFDPC/graph.json","events_json":"https://pith.science/api/pith-number/757IGEGNERM2DBVG2J3ONJFDPC/events.json","paper":"https://pith.science/paper/757IGEGN"},"agent_actions":{"view_html":"https://pith.science/pith/757IGEGNERM2DBVG2J3ONJFDPC","download_json":"https://pith.science/pith/757IGEGNERM2DBVG2J3ONJFDPC.json","view_paper":"https://pith.science/paper/757IGEGN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.17299&json=true","fetch_graph":"https://pith.science/api/pith-number/757IGEGNERM2DBVG2J3ONJFDPC/graph.json","fetch_events":"https://pith.science/api/pith-number/757IGEGNERM2DBVG2J3ONJFDPC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/757IGEGNERM2DBVG2J3ONJFDPC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/757IGEGNERM2DBVG2J3ONJFDPC/action/storage_attestation","attest_author":"https://pith.science/pith/757IGEGNERM2DBVG2J3ONJFDPC/action/author_attestation","sign_citation":"https://pith.science/pith/757IGEGNERM2DBVG2J3ONJFDPC/action/citation_signature","submit_replication":"https://pith.science/pith/757IGEGNERM2DBVG2J3ONJFDPC/action/replication_record"}},"created_at":"2026-07-05T08:00:48.541231+00:00","updated_at":"2026-07-05T08:00:48.541231+00:00"}