{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MQ75HNHRKB545GRODRNZ3XWKMQ","short_pith_number":"pith:MQ75HNHR","schema_version":"1.0","canonical_sha256":"643fd3b4f1507bce9a2e1c5b9ddeca64141355645c5ddd292d00a79561a84a3b","source":{"kind":"arxiv","id":"2410.12207","version":2},"attestation_state":"computed","paper":{"title":"Divide-Verify-Refine: Can LLMs Self-Align with Complex Instructions?","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Dongwon Lee, Hui Liu, Qi He, Suhang Wang, Xianfeng Tang, Xianren Zhang, Zongyu Wu","submitted_at":"2024-10-16T04:01:55Z","abstract_excerpt":"Recent studies show LLMs struggle with complex instructions involving multiple constraints (e.g., length, format, sentiment). Existing works address this issue by fine-tuning, which heavily relies on fine-tuning data quality and is computational expensive. An alternative is leveraging LLMs' self-correction to refine responses for better constraint adherence. However, this is limited by the feedback quality, as LLMs cannot generate reliable feedback or detect errors. Moreover, its effectiveness relies on few-shot examples illustrating response modifications. As constraints in complex instructio"},"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":"2410.12207","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2024-10-16T04:01:55Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"305a7a3b6f71b2ea247a88beee15ff516caa5be9acf93dc2908383dd55f4d331","abstract_canon_sha256":"7c49fcf6787896364d80d723ad7ba23ea076f3533a6cd022c40340e7a1a166ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:21:24.703994Z","signature_b64":"HankHnm5D0+V+vwlepkmgFW5b8JgpPT77lxgowV0/QoJEA855/dnxkB1blt5PRYKfsDr9v/rAoPLtn4480RWDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"643fd3b4f1507bce9a2e1c5b9ddeca64141355645c5ddd292d00a79561a84a3b","last_reissued_at":"2026-07-05T10:21:24.703176Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:21:24.703176Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Divide-Verify-Refine: Can LLMs Self-Align with Complex Instructions?","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Dongwon Lee, Hui Liu, Qi He, Suhang Wang, Xianfeng Tang, Xianren Zhang, Zongyu Wu","submitted_at":"2024-10-16T04:01:55Z","abstract_excerpt":"Recent studies show LLMs struggle with complex instructions involving multiple constraints (e.g., length, format, sentiment). Existing works address this issue by fine-tuning, which heavily relies on fine-tuning data quality and is computational expensive. An alternative is leveraging LLMs' self-correction to refine responses for better constraint adherence. However, this is limited by the feedback quality, as LLMs cannot generate reliable feedback or detect errors. Moreover, its effectiveness relies on few-shot examples illustrating response modifications. As constraints in complex instructio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.12207","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/2410.12207/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":"2410.12207","created_at":"2026-07-05T10:21:24.703292+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.12207v2","created_at":"2026-07-05T10:21:24.703292+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.12207","created_at":"2026-07-05T10:21:24.703292+00:00"},{"alias_kind":"pith_short_12","alias_value":"MQ75HNHRKB54","created_at":"2026-07-05T10:21:24.703292+00:00"},{"alias_kind":"pith_short_16","alias_value":"MQ75HNHRKB545GRO","created_at":"2026-07-05T10:21:24.703292+00:00"},{"alias_kind":"pith_short_8","alias_value":"MQ75HNHR","created_at":"2026-07-05T10:21:24.703292+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MQ75HNHRKB545GRODRNZ3XWKMQ","json":"https://pith.science/pith/MQ75HNHRKB545GRODRNZ3XWKMQ.json","graph_json":"https://pith.science/api/pith-number/MQ75HNHRKB545GRODRNZ3XWKMQ/graph.json","events_json":"https://pith.science/api/pith-number/MQ75HNHRKB545GRODRNZ3XWKMQ/events.json","paper":"https://pith.science/paper/MQ75HNHR"},"agent_actions":{"view_html":"https://pith.science/pith/MQ75HNHRKB545GRODRNZ3XWKMQ","download_json":"https://pith.science/pith/MQ75HNHRKB545GRODRNZ3XWKMQ.json","view_paper":"https://pith.science/paper/MQ75HNHR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.12207&json=true","fetch_graph":"https://pith.science/api/pith-number/MQ75HNHRKB545GRODRNZ3XWKMQ/graph.json","fetch_events":"https://pith.science/api/pith-number/MQ75HNHRKB545GRODRNZ3XWKMQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MQ75HNHRKB545GRODRNZ3XWKMQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MQ75HNHRKB545GRODRNZ3XWKMQ/action/storage_attestation","attest_author":"https://pith.science/pith/MQ75HNHRKB545GRODRNZ3XWKMQ/action/author_attestation","sign_citation":"https://pith.science/pith/MQ75HNHRKB545GRODRNZ3XWKMQ/action/citation_signature","submit_replication":"https://pith.science/pith/MQ75HNHRKB545GRODRNZ3XWKMQ/action/replication_record"}},"created_at":"2026-07-05T10:21:24.703292+00:00","updated_at":"2026-07-05T10:21:24.703292+00:00"}