{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:3ZWTTWARV47CZ3FQ66DJQTY2HY","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":"43df85d0f5ff7c458c69ced4b32a38c9cd65dab79948c61662d41d1376a0f407","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-31T18:09:51Z","title_canon_sha256":"41acaf5b55604fa241e9931b0e7455dad28c5da4949adc942b60ef83a231ff00"},"schema_version":"1.0","source":{"id":"2211.00053","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.00053","created_at":"2026-07-05T05:12:17Z"},{"alias_kind":"arxiv_version","alias_value":"2211.00053v1","created_at":"2026-07-05T05:12:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.00053","created_at":"2026-07-05T05:12:17Z"},{"alias_kind":"pith_short_12","alias_value":"3ZWTTWARV47C","created_at":"2026-07-05T05:12:17Z"},{"alias_kind":"pith_short_16","alias_value":"3ZWTTWARV47CZ3FQ","created_at":"2026-07-05T05:12:17Z"},{"alias_kind":"pith_short_8","alias_value":"3ZWTTWAR","created_at":"2026-07-05T05:12:17Z"}],"graph_snapshots":[{"event_id":"sha256:4539d3781037d0ed76375fd15baac252985c22fca1e067129ff0cc04f51f76af","target":"graph","created_at":"2026-07-05T05:12:17Z","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/2211.00053/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Daniel Khashabi, Faeze Brahman, Peter West, Sean Welleck, Tianxiao Shen, Ximing Lu, Yejin Choi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-31T18:09:51Z","title":"Generating Sequences by Learning to Self-Correct"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.00053","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":"sha256:610f2c1f16cc67d4b3c6651a89470857a6d40e65b61cf554a0e021b634d3b65f","target":"record","created_at":"2026-07-05T05:12:17Z","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":"43df85d0f5ff7c458c69ced4b32a38c9cd65dab79948c61662d41d1376a0f407","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-31T18:09:51Z","title_canon_sha256":"41acaf5b55604fa241e9931b0e7455dad28c5da4949adc942b60ef83a231ff00"},"schema_version":"1.0","source":{"id":"2211.00053","kind":"arxiv","version":1}},"canonical_sha256":"de6d39d811af3e2cecb0f786984f1a3e04d90fddf733093faf45ecad5e4f8348","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"de6d39d811af3e2cecb0f786984f1a3e04d90fddf733093faf45ecad5e4f8348","first_computed_at":"2026-07-05T05:12:17.842132Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:12:17.842132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2NA7rQcXes7hNv4sI5E5pOn0mdJptLwup6bPWS6enjg3YA9eMmeHLoTSUYWfdyqr9cVkETzZDTTsV8ahBnHHCA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:12:17.842601Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.00053","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:610f2c1f16cc67d4b3c6651a89470857a6d40e65b61cf554a0e021b634d3b65f","sha256:4539d3781037d0ed76375fd15baac252985c22fca1e067129ff0cc04f51f76af"],"state_sha256":"1a7a1d0b0a8a38fc68fcb4c87fbce49f973f2eea8dcdde6ee798200fd6591232"}