{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4A44J5FZOUPVPITVLIOBBYCC5I","short_pith_number":"pith:4A44J5FZ","schema_version":"1.0","canonical_sha256":"e039c4f4b9751f57a2755a1c10e042ea1b8486cd66a2f9296b6433c9493007ac","source":{"kind":"arxiv","id":"2410.12052","version":1},"attestation_state":"computed","paper":{"title":"Skill-LLM: Repurposing General-Purpose LLMs for Skill Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Amirhossein Herandi, Xiao Cai, Ximin Hu, Yitao Li, Zhanlin Liu","submitted_at":"2024-10-15T20:41:18Z","abstract_excerpt":"Accurate skill extraction from job descriptions is crucial in the hiring process but remains challenging. Named Entity Recognition (NER) is a common approach used to address this issue. With the demonstrated success of large language models (LLMs) in various NLP tasks, including NER, we propose fine-tuning a specialized Skill-LLM and a light weight model to improve the precision and quality of skill extraction. In our study, we evaluated the fine-tuned Skill-LLM and the light weight model using a benchmark dataset and compared its performance against state-of-the-art (SOTA) methods. Our result"},"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":"2410.12052","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-15T20:41:18Z","cross_cats_sorted":[],"title_canon_sha256":"9e7fbb6ef8ee17fdec2f629f7fea2d9a3909de4cfdc4934e7b6d2d75281d0cf9","abstract_canon_sha256":"55d0c9c8da153ec2010abbb8358430b880108ac3621722946ac9799a81754411"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:21:13.374859Z","signature_b64":"3kx3R3XGy3zIQo/FV7CEnHIxKRxXTQM4QDzcsfuX4dvpqS3+G/ifmK1MmxSKepqgQbLmVNyRAfgFRdkGNwXGDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e039c4f4b9751f57a2755a1c10e042ea1b8486cd66a2f9296b6433c9493007ac","last_reissued_at":"2026-07-05T09:21:13.374400Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:21:13.374400Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Skill-LLM: Repurposing General-Purpose LLMs for Skill Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Amirhossein Herandi, Xiao Cai, Ximin Hu, Yitao Li, Zhanlin Liu","submitted_at":"2024-10-15T20:41:18Z","abstract_excerpt":"Accurate skill extraction from job descriptions is crucial in the hiring process but remains challenging. Named Entity Recognition (NER) is a common approach used to address this issue. With the demonstrated success of large language models (LLMs) in various NLP tasks, including NER, we propose fine-tuning a specialized Skill-LLM and a light weight model to improve the precision and quality of skill extraction. In our study, we evaluated the fine-tuned Skill-LLM and the light weight model using a benchmark dataset and compared its performance against state-of-the-art (SOTA) methods. Our result"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.12052","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/2410.12052/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":"2410.12052","created_at":"2026-07-05T09:21:13.374455+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.12052v1","created_at":"2026-07-05T09:21:13.374455+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.12052","created_at":"2026-07-05T09:21:13.374455+00:00"},{"alias_kind":"pith_short_12","alias_value":"4A44J5FZOUPV","created_at":"2026-07-05T09:21:13.374455+00:00"},{"alias_kind":"pith_short_16","alias_value":"4A44J5FZOUPVPITV","created_at":"2026-07-05T09:21:13.374455+00:00"},{"alias_kind":"pith_short_8","alias_value":"4A44J5FZ","created_at":"2026-07-05T09:21:13.374455+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22769","citing_title":"Noise is Signal: Density-Based Outliers as Leading Indicators of Occupational Emergence in Labor Market Text","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4A44J5FZOUPVPITVLIOBBYCC5I","json":"https://pith.science/pith/4A44J5FZOUPVPITVLIOBBYCC5I.json","graph_json":"https://pith.science/api/pith-number/4A44J5FZOUPVPITVLIOBBYCC5I/graph.json","events_json":"https://pith.science/api/pith-number/4A44J5FZOUPVPITVLIOBBYCC5I/events.json","paper":"https://pith.science/paper/4A44J5FZ"},"agent_actions":{"view_html":"https://pith.science/pith/4A44J5FZOUPVPITVLIOBBYCC5I","download_json":"https://pith.science/pith/4A44J5FZOUPVPITVLIOBBYCC5I.json","view_paper":"https://pith.science/paper/4A44J5FZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.12052&json=true","fetch_graph":"https://pith.science/api/pith-number/4A44J5FZOUPVPITVLIOBBYCC5I/graph.json","fetch_events":"https://pith.science/api/pith-number/4A44J5FZOUPVPITVLIOBBYCC5I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4A44J5FZOUPVPITVLIOBBYCC5I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4A44J5FZOUPVPITVLIOBBYCC5I/action/storage_attestation","attest_author":"https://pith.science/pith/4A44J5FZOUPVPITVLIOBBYCC5I/action/author_attestation","sign_citation":"https://pith.science/pith/4A44J5FZOUPVPITVLIOBBYCC5I/action/citation_signature","submit_replication":"https://pith.science/pith/4A44J5FZOUPVPITVLIOBBYCC5I/action/replication_record"}},"created_at":"2026-07-05T09:21:13.374455+00:00","updated_at":"2026-07-05T09:21:13.374455+00:00"}