{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:3ZWTTWARV47CZ3FQ66DJQTY2HY","short_pith_number":"pith:3ZWTTWAR","schema_version":"1.0","canonical_sha256":"de6d39d811af3e2cecb0f786984f1a3e04d90fddf733093faf45ecad5e4f8348","source":{"kind":"arxiv","id":"2211.00053","version":1},"attestation_state":"computed","paper":{"title":"Generating Sequences by Learning to Self-Correct","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daniel Khashabi, Faeze Brahman, Peter West, Sean Welleck, Tianxiao Shen, Ximing Lu, Yejin Choi","submitted_at":"2022-10-31T18:09:51Z","abstract_excerpt":"Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content. Language models, whether fine-tuned or prompted with few-shot demonstrations, frequently violate these constraints, and lack a mechanism to iteratively revise their outputs. Moreover, some powerful language models are of extreme scale or inaccessible, making it inefficient, if not infeasible, to update their parameters for task-specific adaptation. We present Self-Correction, an approach that decouples an imperfect base g"},"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":"2211.00053","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-31T18:09:51Z","cross_cats_sorted":[],"title_canon_sha256":"41acaf5b55604fa241e9931b0e7455dad28c5da4949adc942b60ef83a231ff00","abstract_canon_sha256":"43df85d0f5ff7c458c69ced4b32a38c9cd65dab79948c61662d41d1376a0f407"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:12:17.842601Z","signature_b64":"2NA7rQcXes7hNv4sI5E5pOn0mdJptLwup6bPWS6enjg3YA9eMmeHLoTSUYWfdyqr9cVkETzZDTTsV8ahBnHHCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de6d39d811af3e2cecb0f786984f1a3e04d90fddf733093faf45ecad5e4f8348","last_reissued_at":"2026-07-05T05:12:17.842132Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:12:17.842132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generating Sequences by Learning to Self-Correct","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daniel Khashabi, Faeze Brahman, Peter West, Sean Welleck, Tianxiao Shen, Ximing Lu, Yejin Choi","submitted_at":"2022-10-31T18:09:51Z","abstract_excerpt":"Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content. Language models, whether fine-tuned or prompted with few-shot demonstrations, frequently violate these constraints, and lack a mechanism to iteratively revise their outputs. Moreover, some powerful language models are of extreme scale or inaccessible, making it inefficient, if not infeasible, to update their parameters for task-specific adaptation. We present Self-Correction, an approach that decouples an imperfect base g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.00053","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/2211.00053/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":"2211.00053","created_at":"2026-07-05T05:12:17.842188+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.00053v1","created_at":"2026-07-05T05:12:17.842188+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.00053","created_at":"2026-07-05T05:12:17.842188+00:00"},{"alias_kind":"pith_short_12","alias_value":"3ZWTTWARV47C","created_at":"2026-07-05T05:12:17.842188+00:00"},{"alias_kind":"pith_short_16","alias_value":"3ZWTTWARV47CZ3FQ","created_at":"2026-07-05T05:12:17.842188+00:00"},{"alias_kind":"pith_short_8","alias_value":"3ZWTTWAR","created_at":"2026-07-05T05:12:17.842188+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":13,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.08068","citing_title":"DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination","ref_index":96,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24172","citing_title":"EPPC-OASIS: Ontology-Aware Adaptation and Structured Inference Refinement for Electronic Patient-Provider Communication Mining in Secure Messages","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28421","citing_title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2605.31455","citing_title":"DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18851","citing_title":"STRIDE: Learnable Stepwise Language Feedback for LLM Reasoning","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2508.15815","citing_title":"User-Assistant Bias in LLMs","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2303.17491","citing_title":"Language Models can Solve Computer Tasks","ref_index":74,"is_internal_anchor":false},{"citing_arxiv_id":"2305.14992","citing_title":"Reasoning with Language Model is Planning with World Model","ref_index":96,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21268","citing_title":"Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05477","citing_title":"Don't Act Blindly: Robust GUI Automation via Action-Effect Verification and Self-Correction","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2303.17651","citing_title":"Self-Refine: Iterative Refinement with Self-Feedback","ref_index":49,"is_internal_anchor":false},{"citing_arxiv_id":"2403.07974","citing_title":"LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code","ref_index":141,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02236","citing_title":"Perturbation Dose Responses in Recursive LLM Loops: Raw Switching, Stochastic Floors, and Persistent Escape under Append, Replace, and Dialog Updates","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3ZWTTWARV47CZ3FQ66DJQTY2HY","json":"https://pith.science/pith/3ZWTTWARV47CZ3FQ66DJQTY2HY.json","graph_json":"https://pith.science/api/pith-number/3ZWTTWARV47CZ3FQ66DJQTY2HY/graph.json","events_json":"https://pith.science/api/pith-number/3ZWTTWARV47CZ3FQ66DJQTY2HY/events.json","paper":"https://pith.science/paper/3ZWTTWAR"},"agent_actions":{"view_html":"https://pith.science/pith/3ZWTTWARV47CZ3FQ66DJQTY2HY","download_json":"https://pith.science/pith/3ZWTTWARV47CZ3FQ66DJQTY2HY.json","view_paper":"https://pith.science/paper/3ZWTTWAR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.00053&json=true","fetch_graph":"https://pith.science/api/pith-number/3ZWTTWARV47CZ3FQ66DJQTY2HY/graph.json","fetch_events":"https://pith.science/api/pith-number/3ZWTTWARV47CZ3FQ66DJQTY2HY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3ZWTTWARV47CZ3FQ66DJQTY2HY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3ZWTTWARV47CZ3FQ66DJQTY2HY/action/storage_attestation","attest_author":"https://pith.science/pith/3ZWTTWARV47CZ3FQ66DJQTY2HY/action/author_attestation","sign_citation":"https://pith.science/pith/3ZWTTWARV47CZ3FQ66DJQTY2HY/action/citation_signature","submit_replication":"https://pith.science/pith/3ZWTTWARV47CZ3FQ66DJQTY2HY/action/replication_record"}},"created_at":"2026-07-05T05:12:17.842188+00:00","updated_at":"2026-07-05T05:12:17.842188+00:00"}