{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HIVF3FAC45LT4UKQ67SZNX7QPR","short_pith_number":"pith:HIVF3FAC","schema_version":"1.0","canonical_sha256":"3a2a5d9402e7573e5150f7e596dff07c7bf7fd3ddfc8def794e76e1f0d8c60e9","source":{"kind":"arxiv","id":"2402.12419","version":1},"attestation_state":"computed","paper":{"title":"EBFT: Effective and Block-Wise Fine-Tuning for Sparse LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Fan Wu, Fei Chao, Lei Zhang, Rongrong Ji, Shengchuan Zhang, Song Guo, Xiawu Zheng, Yiyu Shi","submitted_at":"2024-02-19T09:55:32Z","abstract_excerpt":"Existing methods for fine-tuning sparse LLMs often suffer from resource-intensive requirements and high retraining costs. Additionally, many fine-tuning methods often rely on approximations or heuristic optimization strategies, which may lead to suboptimal solutions. To address these issues, we propose an efficient and fast framework for fine-tuning sparse LLMs based on minimizing reconstruction error. Our approach involves sampling a small dataset for calibration and utilizing backpropagation to iteratively optimize block-wise reconstruction error, on a block-by-block basis, aiming for optima"},"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":"2402.12419","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-19T09:55:32Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"f385892499068b6d79bb841be25fbea669038e7b36fcc2b8645c6c09b3afaf70","abstract_canon_sha256":"22f76d9e34afcabed5bac54b1e424bc3549a3c740c4f54e37e941b0d118d6a2e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:11.036310Z","signature_b64":"1UbH0DtLcEYiwgA4JEtRP3iplF7DfyMECp1Uss/Lap55zL0qBFrZ1gr7SVabw+6jy0R9GWAnkq3z3KskH6iPAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a2a5d9402e7573e5150f7e596dff07c7bf7fd3ddfc8def794e76e1f0d8c60e9","last_reissued_at":"2026-07-05T07:47:11.035843Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:11.035843Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EBFT: Effective and Block-Wise Fine-Tuning for Sparse LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Fan Wu, Fei Chao, Lei Zhang, Rongrong Ji, Shengchuan Zhang, Song Guo, Xiawu Zheng, Yiyu Shi","submitted_at":"2024-02-19T09:55:32Z","abstract_excerpt":"Existing methods for fine-tuning sparse LLMs often suffer from resource-intensive requirements and high retraining costs. Additionally, many fine-tuning methods often rely on approximations or heuristic optimization strategies, which may lead to suboptimal solutions. To address these issues, we propose an efficient and fast framework for fine-tuning sparse LLMs based on minimizing reconstruction error. Our approach involves sampling a small dataset for calibration and utilizing backpropagation to iteratively optimize block-wise reconstruction error, on a block-by-block basis, aiming for optima"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.12419","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/2402.12419/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":"2402.12419","created_at":"2026-07-05T07:47:11.035894+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.12419v1","created_at":"2026-07-05T07:47:11.035894+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.12419","created_at":"2026-07-05T07:47:11.035894+00:00"},{"alias_kind":"pith_short_12","alias_value":"HIVF3FAC45LT","created_at":"2026-07-05T07:47:11.035894+00:00"},{"alias_kind":"pith_short_16","alias_value":"HIVF3FAC45LT4UKQ","created_at":"2026-07-05T07:47:11.035894+00:00"},{"alias_kind":"pith_short_8","alias_value":"HIVF3FAC","created_at":"2026-07-05T07:47:11.035894+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2507.03052","citing_title":"From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HIVF3FAC45LT4UKQ67SZNX7QPR","json":"https://pith.science/pith/HIVF3FAC45LT4UKQ67SZNX7QPR.json","graph_json":"https://pith.science/api/pith-number/HIVF3FAC45LT4UKQ67SZNX7QPR/graph.json","events_json":"https://pith.science/api/pith-number/HIVF3FAC45LT4UKQ67SZNX7QPR/events.json","paper":"https://pith.science/paper/HIVF3FAC"},"agent_actions":{"view_html":"https://pith.science/pith/HIVF3FAC45LT4UKQ67SZNX7QPR","download_json":"https://pith.science/pith/HIVF3FAC45LT4UKQ67SZNX7QPR.json","view_paper":"https://pith.science/paper/HIVF3FAC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.12419&json=true","fetch_graph":"https://pith.science/api/pith-number/HIVF3FAC45LT4UKQ67SZNX7QPR/graph.json","fetch_events":"https://pith.science/api/pith-number/HIVF3FAC45LT4UKQ67SZNX7QPR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HIVF3FAC45LT4UKQ67SZNX7QPR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HIVF3FAC45LT4UKQ67SZNX7QPR/action/storage_attestation","attest_author":"https://pith.science/pith/HIVF3FAC45LT4UKQ67SZNX7QPR/action/author_attestation","sign_citation":"https://pith.science/pith/HIVF3FAC45LT4UKQ67SZNX7QPR/action/citation_signature","submit_replication":"https://pith.science/pith/HIVF3FAC45LT4UKQ67SZNX7QPR/action/replication_record"}},"created_at":"2026-07-05T07:47:11.035894+00:00","updated_at":"2026-07-05T07:47:11.035894+00:00"}