{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BI3KOD5SSJ7OJZFSJX6PLIWCRH","short_pith_number":"pith:BI3KOD5S","schema_version":"1.0","canonical_sha256":"0a36a70fb2927ee4e4b24dfcf5a2c289d7502d622af919fb9802d8f477c10f15","source":{"kind":"arxiv","id":"2403.15736","version":2},"attestation_state":"computed","paper":{"title":"General LLMs as Instructors for Domain-Specific LLMs: A Sequential Fusion Method to Integrate Extraction and Editing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Huijia Liang, Qin Zhang, Tianjie Ju, Xin Zhang, Ying Fu","submitted_at":"2024-03-23T06:03:36Z","abstract_excerpt":"The substantial interest in updating Large Language Models (LLMs) without retraining from scratch is accompanied by several challenges. This is particularly true when updating LLMs with datasets that necessitate domain-expert reasoning across extensive texts, despite limited samples. We termed the scenario as the Few-Shot Domain-Expert Reasoning for Updating LLMs (FDoR-UL). Traditional methods such as Low-Rank Adaptation (LoRA) and Retrieval Augmented Generation (RAG) are inadequate for addressing this critical issue, particularly evident in our exploration of a specific medical dataset that e"},"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":"2403.15736","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-23T06:03:36Z","cross_cats_sorted":[],"title_canon_sha256":"12700ec05036d34a5af5ec73462386ef7b6514af4918cab7ad7eb1732b607f52","abstract_canon_sha256":"a85e15dbebd594f38f6c31e59f1a9ad0b1c137badc03ff513f31104a5d47fe3a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:34:56.169111Z","signature_b64":"wLYfI+F0uB+xBOG0WJpAySrX7mnwMF7KSmtIi5iZRy69hhvC4y47+GDcNpSUknNbxlhOWD6Y+34uby4eYE9YCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a36a70fb2927ee4e4b24dfcf5a2c289d7502d622af919fb9802d8f477c10f15","last_reissued_at":"2026-07-05T09:34:56.168650Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:34:56.168650Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"General LLMs as Instructors for Domain-Specific LLMs: A Sequential Fusion Method to Integrate Extraction and Editing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Huijia Liang, Qin Zhang, Tianjie Ju, Xin Zhang, Ying Fu","submitted_at":"2024-03-23T06:03:36Z","abstract_excerpt":"The substantial interest in updating Large Language Models (LLMs) without retraining from scratch is accompanied by several challenges. This is particularly true when updating LLMs with datasets that necessitate domain-expert reasoning across extensive texts, despite limited samples. We termed the scenario as the Few-Shot Domain-Expert Reasoning for Updating LLMs (FDoR-UL). Traditional methods such as Low-Rank Adaptation (LoRA) and Retrieval Augmented Generation (RAG) are inadequate for addressing this critical issue, particularly evident in our exploration of a specific medical dataset that e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.15736","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/2403.15736/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":"2403.15736","created_at":"2026-07-05T09:34:56.168709+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.15736v2","created_at":"2026-07-05T09:34:56.168709+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.15736","created_at":"2026-07-05T09:34:56.168709+00:00"},{"alias_kind":"pith_short_12","alias_value":"BI3KOD5SSJ7O","created_at":"2026-07-05T09:34:56.168709+00:00"},{"alias_kind":"pith_short_16","alias_value":"BI3KOD5SSJ7OJZFS","created_at":"2026-07-05T09:34:56.168709+00:00"},{"alias_kind":"pith_short_8","alias_value":"BI3KOD5S","created_at":"2026-07-05T09:34:56.168709+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.17484","citing_title":"From Unstructured Communication to Intelligent RAG: Multi-Agent Automation for Supply Chain Knowledge Bases","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH","json":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH.json","graph_json":"https://pith.science/api/pith-number/BI3KOD5SSJ7OJZFSJX6PLIWCRH/graph.json","events_json":"https://pith.science/api/pith-number/BI3KOD5SSJ7OJZFSJX6PLIWCRH/events.json","paper":"https://pith.science/paper/BI3KOD5S"},"agent_actions":{"view_html":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH","download_json":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH.json","view_paper":"https://pith.science/paper/BI3KOD5S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.15736&json=true","fetch_graph":"https://pith.science/api/pith-number/BI3KOD5SSJ7OJZFSJX6PLIWCRH/graph.json","fetch_events":"https://pith.science/api/pith-number/BI3KOD5SSJ7OJZFSJX6PLIWCRH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH/action/storage_attestation","attest_author":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH/action/author_attestation","sign_citation":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH/action/citation_signature","submit_replication":"https://pith.science/pith/BI3KOD5SSJ7OJZFSJX6PLIWCRH/action/replication_record"}},"created_at":"2026-07-05T09:34:56.168709+00:00","updated_at":"2026-07-05T09:34:56.168709+00:00"}