{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:A36SAID56BVEYBTLWGTPQRLY6F","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3fde1b83c69f9bf294059b2cdbb4f3eec71706c60238d5ff34a87680817fb0e1","cross_cats_sorted":["cs.SY","eess.SP","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-02T23:18:11Z","title_canon_sha256":"65df9878258560fe555edfb5aa00fa26c8d86ceeccbeaf7eaa75313e7ada672c"},"schema_version":"1.0","source":{"id":"2406.02479","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.02479","created_at":"2026-07-05T08:27:20Z"},{"alias_kind":"arxiv_version","alias_value":"2406.02479v1","created_at":"2026-07-05T08:27:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.02479","created_at":"2026-07-05T08:27:20Z"},{"alias_kind":"pith_short_12","alias_value":"A36SAID56BVE","created_at":"2026-07-05T08:27:20Z"},{"alias_kind":"pith_short_16","alias_value":"A36SAID56BVEYBTL","created_at":"2026-07-05T08:27:20Z"},{"alias_kind":"pith_short_8","alias_value":"A36SAID5","created_at":"2026-07-05T08:27:20Z"}],"graph_snapshots":[{"event_id":"sha256:77be99e31d4d012ffc8bbf46c87813b227abf026b5d75b458b7ffd34687da9f0","target":"graph","created_at":"2026-07-05T08:27:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2406.02479/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper presents a novel method for utilizing fine-tuned Large Language Models (LLMs) to minimize data requirements in load profile analysis, demonstrated through the restoration of missing data in power system load profiles. A two-stage fine-tuning strategy is proposed to adapt a pre-trained LLMs, i.e., GPT-3.5, for missing data restoration tasks. Through empirical evaluation, we demonstrate the effectiveness of the fine-tuned model in accurately restoring missing data, achieving comparable performance to state-of-the-art specifically designed models such as BERT-PIN. Key findings include ","authors_text":"Hyeonjin Kim, Kai Ye, Ning Lu, Yi Hu","cross_cats":["cs.SY","eess.SP","eess.SY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-02T23:18:11Z","title":"Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.02479","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:956a30af35793c43c0c7a58343835c050bdd5c5b385a906067b869060a9e53e6","target":"record","created_at":"2026-07-05T08:27:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3fde1b83c69f9bf294059b2cdbb4f3eec71706c60238d5ff34a87680817fb0e1","cross_cats_sorted":["cs.SY","eess.SP","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-02T23:18:11Z","title_canon_sha256":"65df9878258560fe555edfb5aa00fa26c8d86ceeccbeaf7eaa75313e7ada672c"},"schema_version":"1.0","source":{"id":"2406.02479","kind":"arxiv","version":1}},"canonical_sha256":"06fd20207df06a4c066bb1a6f84578f14a963c31d986941149ae7a392b35c0f7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"06fd20207df06a4c066bb1a6f84578f14a963c31d986941149ae7a392b35c0f7","first_computed_at":"2026-07-05T08:27:20.400483Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:27:20.400483Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z0o5QnYzqlWKq9Z5AbwhRSykEcq3R1Nm3KDeDMYJObINgNkciQR0jj+LaGUgwSbNCEzeEMrn9BUaoXVrvq+NDw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:27:20.400888Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.02479","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:956a30af35793c43c0c7a58343835c050bdd5c5b385a906067b869060a9e53e6","sha256:77be99e31d4d012ffc8bbf46c87813b227abf026b5d75b458b7ffd34687da9f0"],"state_sha256":"4c1d8382264ba7b5cd265a62320ef874f1cb204e1a06c35262346f1137048b5f"}