{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:LFEO4TXEUABHTCVSSPOVOZFGNR","short_pith_number":"pith:LFEO4TXE","schema_version":"1.0","canonical_sha256":"5948ee4ee4a002798ab293dd5764a66c7eb9878ec65e28ae42ff883c02c2fb61","source":{"kind":"arxiv","id":"2210.02768","version":1},"attestation_state":"computed","paper":{"title":"Distilling Task-specific Logical Rules from Large Pre-trained Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Baoliang Cui, Haihong Tang, LuXin Liu, Siliang Tang, Tao Chen, Xuepeng Jia","submitted_at":"2022-10-06T09:12:18Z","abstract_excerpt":"Logical rules, both transferable and explainable, are widely used as weakly supervised signals for many downstream tasks such as named entity tagging. To reduce the human effort of writing rules, previous researchers adopt an iterative approach to automatically learn logical rules from several seed rules. However, obtaining more seed rules can only be accomplished by extra human annotation with heavy costs. Limited by the size and quality of the seed rules, the model performance of previous systems is bounded. In this paper, we develop a novel framework STREAM to distill task-specific logical "},"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":"2210.02768","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-06T09:12:18Z","cross_cats_sorted":[],"title_canon_sha256":"80de2be42578e00899d5d61abd4a037dfefff3d0dddaacb8328decd949771564","abstract_canon_sha256":"e178c3d651cf3097fc9d4b6fdfc5c1bbc61211e8a26fe17f552e64bda067be18"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:03:57.071728Z","signature_b64":"5zmD6mHMV9dtHquR05x9j5b4aVBq15C7WTVlZrlhTu9Z2Pn22tr6YRt4QUX8hzDVVNSeeI0g/CDhaY9qEQs1Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5948ee4ee4a002798ab293dd5764a66c7eb9878ec65e28ae42ff883c02c2fb61","last_reissued_at":"2026-07-05T05:03:57.071121Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:03:57.071121Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distilling Task-specific Logical Rules from Large Pre-trained Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Baoliang Cui, Haihong Tang, LuXin Liu, Siliang Tang, Tao Chen, Xuepeng Jia","submitted_at":"2022-10-06T09:12:18Z","abstract_excerpt":"Logical rules, both transferable and explainable, are widely used as weakly supervised signals for many downstream tasks such as named entity tagging. To reduce the human effort of writing rules, previous researchers adopt an iterative approach to automatically learn logical rules from several seed rules. However, obtaining more seed rules can only be accomplished by extra human annotation with heavy costs. Limited by the size and quality of the seed rules, the model performance of previous systems is bounded. In this paper, we develop a novel framework STREAM to distill task-specific logical "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.02768","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/2210.02768/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":"2210.02768","created_at":"2026-07-05T05:03:57.071206+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.02768v1","created_at":"2026-07-05T05:03:57.071206+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.02768","created_at":"2026-07-05T05:03:57.071206+00:00"},{"alias_kind":"pith_short_12","alias_value":"LFEO4TXEUABH","created_at":"2026-07-05T05:03:57.071206+00:00"},{"alias_kind":"pith_short_16","alias_value":"LFEO4TXEUABHTCVS","created_at":"2026-07-05T05:03:57.071206+00:00"},{"alias_kind":"pith_short_8","alias_value":"LFEO4TXE","created_at":"2026-07-05T05:03:57.071206+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LFEO4TXEUABHTCVSSPOVOZFGNR","json":"https://pith.science/pith/LFEO4TXEUABHTCVSSPOVOZFGNR.json","graph_json":"https://pith.science/api/pith-number/LFEO4TXEUABHTCVSSPOVOZFGNR/graph.json","events_json":"https://pith.science/api/pith-number/LFEO4TXEUABHTCVSSPOVOZFGNR/events.json","paper":"https://pith.science/paper/LFEO4TXE"},"agent_actions":{"view_html":"https://pith.science/pith/LFEO4TXEUABHTCVSSPOVOZFGNR","download_json":"https://pith.science/pith/LFEO4TXEUABHTCVSSPOVOZFGNR.json","view_paper":"https://pith.science/paper/LFEO4TXE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.02768&json=true","fetch_graph":"https://pith.science/api/pith-number/LFEO4TXEUABHTCVSSPOVOZFGNR/graph.json","fetch_events":"https://pith.science/api/pith-number/LFEO4TXEUABHTCVSSPOVOZFGNR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LFEO4TXEUABHTCVSSPOVOZFGNR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LFEO4TXEUABHTCVSSPOVOZFGNR/action/storage_attestation","attest_author":"https://pith.science/pith/LFEO4TXEUABHTCVSSPOVOZFGNR/action/author_attestation","sign_citation":"https://pith.science/pith/LFEO4TXEUABHTCVSSPOVOZFGNR/action/citation_signature","submit_replication":"https://pith.science/pith/LFEO4TXEUABHTCVSSPOVOZFGNR/action/replication_record"}},"created_at":"2026-07-05T05:03:57.071206+00:00","updated_at":"2026-07-05T05:03:57.071206+00:00"}