{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WLI2DBSNFGK5VKXSNHXKMWU4MV","short_pith_number":"pith:WLI2DBSN","schema_version":"1.0","canonical_sha256":"b2d1a1864d2995daaaf269eea65a9c655004a67aa1b50e2734fd28c3e96b4d80","source":{"kind":"arxiv","id":"2411.00451","version":1},"attestation_state":"computed","paper":{"title":"Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Neeraj Agrawal, Subhadip Nandi","submitted_at":"2024-11-01T08:57:29Z","abstract_excerpt":"Few-Shot Cross-Domain NER is the process of leveraging knowledge from data-rich source domains to perform entity recognition on data scarce target domains. Most previous state-of-the-art (SOTA) approaches use pre-trained language models (PLMs) for cross-domain NER. However, these models are often domain specific. To successfully use these models for new target domains, we need to modify either the model architecture or perform model finetuning using data from the new domains. Both of these result in the creation of entirely new NER models for each target domain which is infeasible for practica"},"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":"2411.00451","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-01T08:57:29Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"11f6ef848fd7927ba546a4e05265fc9d7a8dd55fc0bda26c5e9dcef77df2ad80","abstract_canon_sha256":"9a0432e2d4dd9dadd208b15e9d4cb922078efd2db10fdc624abf1354a41e0cd2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:01:28.337546Z","signature_b64":"2GG63lbf7TPD+UEe0JLKoPvwzwUOj9yuxFEvkzex/DMvu/EcaxfnDQXZdKMg8ypnoYzOBKwb45kdXsbODo+pCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b2d1a1864d2995daaaf269eea65a9c655004a67aa1b50e2734fd28c3e96b4d80","last_reissued_at":"2026-07-05T11:01:28.337061Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:01:28.337061Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Neeraj Agrawal, Subhadip Nandi","submitted_at":"2024-11-01T08:57:29Z","abstract_excerpt":"Few-Shot Cross-Domain NER is the process of leveraging knowledge from data-rich source domains to perform entity recognition on data scarce target domains. Most previous state-of-the-art (SOTA) approaches use pre-trained language models (PLMs) for cross-domain NER. However, these models are often domain specific. To successfully use these models for new target domains, we need to modify either the model architecture or perform model finetuning using data from the new domains. Both of these result in the creation of entirely new NER models for each target domain which is infeasible for practica"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.00451","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/2411.00451/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":"2411.00451","created_at":"2026-07-05T11:01:28.337117+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.00451v1","created_at":"2026-07-05T11:01:28.337117+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.00451","created_at":"2026-07-05T11:01:28.337117+00:00"},{"alias_kind":"pith_short_12","alias_value":"WLI2DBSNFGK5","created_at":"2026-07-05T11:01:28.337117+00:00"},{"alias_kind":"pith_short_16","alias_value":"WLI2DBSNFGK5VKXS","created_at":"2026-07-05T11:01:28.337117+00:00"},{"alias_kind":"pith_short_8","alias_value":"WLI2DBSN","created_at":"2026-07-05T11:01:28.337117+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.03706","citing_title":"SAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WLI2DBSNFGK5VKXSNHXKMWU4MV","json":"https://pith.science/pith/WLI2DBSNFGK5VKXSNHXKMWU4MV.json","graph_json":"https://pith.science/api/pith-number/WLI2DBSNFGK5VKXSNHXKMWU4MV/graph.json","events_json":"https://pith.science/api/pith-number/WLI2DBSNFGK5VKXSNHXKMWU4MV/events.json","paper":"https://pith.science/paper/WLI2DBSN"},"agent_actions":{"view_html":"https://pith.science/pith/WLI2DBSNFGK5VKXSNHXKMWU4MV","download_json":"https://pith.science/pith/WLI2DBSNFGK5VKXSNHXKMWU4MV.json","view_paper":"https://pith.science/paper/WLI2DBSN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.00451&json=true","fetch_graph":"https://pith.science/api/pith-number/WLI2DBSNFGK5VKXSNHXKMWU4MV/graph.json","fetch_events":"https://pith.science/api/pith-number/WLI2DBSNFGK5VKXSNHXKMWU4MV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WLI2DBSNFGK5VKXSNHXKMWU4MV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WLI2DBSNFGK5VKXSNHXKMWU4MV/action/storage_attestation","attest_author":"https://pith.science/pith/WLI2DBSNFGK5VKXSNHXKMWU4MV/action/author_attestation","sign_citation":"https://pith.science/pith/WLI2DBSNFGK5VKXSNHXKMWU4MV/action/citation_signature","submit_replication":"https://pith.science/pith/WLI2DBSNFGK5VKXSNHXKMWU4MV/action/replication_record"}},"created_at":"2026-07-05T11:01:28.337117+00:00","updated_at":"2026-07-05T11:01:28.337117+00:00"}