{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:RUBOSHF6UWFHOXKY2CZ5ZUFTXS","short_pith_number":"pith:RUBOSHF6","canonical_record":{"source":{"id":"2311.09336","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-15T19:52:11Z","cross_cats_sorted":[],"title_canon_sha256":"0f67d8467770b0c2ddfadfba714e6076c53afe044408c5e5e26fe83651ac872b","abstract_canon_sha256":"c43f064fed7d924d815d74344cd48731cb64616eb76983fdd7be520216d61817"},"schema_version":"1.0"},"canonical_sha256":"8d02e91cbea58a775d58d0b3dcd0b3bca1143eb503506c8a93178c8439187c9e","source":{"kind":"arxiv","id":"2311.09336","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.09336","created_at":"2026-07-05T09:25:36Z"},{"alias_kind":"arxiv_version","alias_value":"2311.09336v5","created_at":"2026-07-05T09:25:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.09336","created_at":"2026-07-05T09:25:36Z"},{"alias_kind":"pith_short_12","alias_value":"RUBOSHF6UWFH","created_at":"2026-07-05T09:25:36Z"},{"alias_kind":"pith_short_16","alias_value":"RUBOSHF6UWFHOXKY","created_at":"2026-07-05T09:25:36Z"},{"alias_kind":"pith_short_8","alias_value":"RUBOSHF6","created_at":"2026-07-05T09:25:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:RUBOSHF6UWFHOXKY2CZ5ZUFTXS","target":"record","payload":{"canonical_record":{"source":{"id":"2311.09336","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-15T19:52:11Z","cross_cats_sorted":[],"title_canon_sha256":"0f67d8467770b0c2ddfadfba714e6076c53afe044408c5e5e26fe83651ac872b","abstract_canon_sha256":"c43f064fed7d924d815d74344cd48731cb64616eb76983fdd7be520216d61817"},"schema_version":"1.0"},"canonical_sha256":"8d02e91cbea58a775d58d0b3dcd0b3bca1143eb503506c8a93178c8439187c9e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:25:36.152819Z","signature_b64":"un6ShDToUJcu7Xe2hZB5yvKQp3uS3R9inFMNj9FEjStvqfSmNtQMkaUU8iwvf+Eh136lITMUjMfF+EpA+LusBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d02e91cbea58a775d58d0b3dcd0b3bca1143eb503506c8a93178c8439187c9e","last_reissued_at":"2026-07-05T09:25:36.152399Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:25:36.152399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.09336","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:25:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s2WIza6mkL3XoNzBDIyvwFGxwNLPrYMAn8Ke8CnZb+jVJBymxlnSxUNiwzp3LItW2jflcpuEEyiTezN5Eai5BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T07:08:22.890829Z"},"content_sha256":"3352fc063c9d999347422db413baa70d5823a3a47c9ae196ac54648c353cc442","schema_version":"1.0","event_id":"sha256:3352fc063c9d999347422db413baa70d5823a3a47c9ae196ac54648c353cc442"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:RUBOSHF6UWFHOXKY2CZ5ZUFTXS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Biao Zhang, Daniel Deutsch, Juraj Juraska, Lei Li, Mara Finkelstein, Markus Freitag, Wenda Xu, William Yang Wang, Zhongtao Liu","submitted_at":"2023-11-15T19:52:11Z","abstract_excerpt":"Recent large language models (LLM) are leveraging human feedback to improve their generation quality. However, human feedback is costly to obtain, especially during inference. In this work, we propose LLMRefine, an inference time optimization method to refine LLM's output. The core idea is to use a learned fine-grained feedback model to pinpoint defects and guide LLM to refine them iteratively. Using original LLM as a proposal of edits, LLMRefine searches for defect-less text via simulated annealing, trading off the exploration and exploitation. We conduct experiments on three text generation "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.09336","kind":"arxiv","version":5},"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/2311.09336/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:25:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SiqPp3tfY77k9ojjnieQycS+Co7eii578cdeUSWmN6a2pEC6Yf7U05qZGKKLn/uMb5IOPJm/tieAgpfChe8HCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T07:08:22.891531Z"},"content_sha256":"3b0677c92ae40c936e1501fb3a38f8ebae6d4b947027428ebf4cbf7c66c46680","schema_version":"1.0","event_id":"sha256:3b0677c92ae40c936e1501fb3a38f8ebae6d4b947027428ebf4cbf7c66c46680"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RUBOSHF6UWFHOXKY2CZ5ZUFTXS/bundle.json","state_url":"https://pith.science/pith/RUBOSHF6UWFHOXKY2CZ5ZUFTXS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RUBOSHF6UWFHOXKY2CZ5ZUFTXS/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T07:08:22Z","links":{"resolver":"https://pith.science/pith/RUBOSHF6UWFHOXKY2CZ5ZUFTXS","bundle":"https://pith.science/pith/RUBOSHF6UWFHOXKY2CZ5ZUFTXS/bundle.json","state":"https://pith.science/pith/RUBOSHF6UWFHOXKY2CZ5ZUFTXS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RUBOSHF6UWFHOXKY2CZ5ZUFTXS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:RUBOSHF6UWFHOXKY2CZ5ZUFTXS","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":"c43f064fed7d924d815d74344cd48731cb64616eb76983fdd7be520216d61817","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-15T19:52:11Z","title_canon_sha256":"0f67d8467770b0c2ddfadfba714e6076c53afe044408c5e5e26fe83651ac872b"},"schema_version":"1.0","source":{"id":"2311.09336","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.09336","created_at":"2026-07-05T09:25:36Z"},{"alias_kind":"arxiv_version","alias_value":"2311.09336v5","created_at":"2026-07-05T09:25:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.09336","created_at":"2026-07-05T09:25:36Z"},{"alias_kind":"pith_short_12","alias_value":"RUBOSHF6UWFH","created_at":"2026-07-05T09:25:36Z"},{"alias_kind":"pith_short_16","alias_value":"RUBOSHF6UWFHOXKY","created_at":"2026-07-05T09:25:36Z"},{"alias_kind":"pith_short_8","alias_value":"RUBOSHF6","created_at":"2026-07-05T09:25:36Z"}],"graph_snapshots":[{"event_id":"sha256:3b0677c92ae40c936e1501fb3a38f8ebae6d4b947027428ebf4cbf7c66c46680","target":"graph","created_at":"2026-07-05T09:25:36Z","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/2311.09336/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent large language models (LLM) are leveraging human feedback to improve their generation quality. However, human feedback is costly to obtain, especially during inference. In this work, we propose LLMRefine, an inference time optimization method to refine LLM's output. The core idea is to use a learned fine-grained feedback model to pinpoint defects and guide LLM to refine them iteratively. Using original LLM as a proposal of edits, LLMRefine searches for defect-less text via simulated annealing, trading off the exploration and exploitation. We conduct experiments on three text generation ","authors_text":"Biao Zhang, Daniel Deutsch, Juraj Juraska, Lei Li, Mara Finkelstein, Markus Freitag, Wenda Xu, William Yang Wang, Zhongtao Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-15T19:52:11Z","title":"LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.09336","kind":"arxiv","version":5},"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":"sha256:3352fc063c9d999347422db413baa70d5823a3a47c9ae196ac54648c353cc442","target":"record","created_at":"2026-07-05T09:25:36Z","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":"c43f064fed7d924d815d74344cd48731cb64616eb76983fdd7be520216d61817","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-15T19:52:11Z","title_canon_sha256":"0f67d8467770b0c2ddfadfba714e6076c53afe044408c5e5e26fe83651ac872b"},"schema_version":"1.0","source":{"id":"2311.09336","kind":"arxiv","version":5}},"canonical_sha256":"8d02e91cbea58a775d58d0b3dcd0b3bca1143eb503506c8a93178c8439187c9e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8d02e91cbea58a775d58d0b3dcd0b3bca1143eb503506c8a93178c8439187c9e","first_computed_at":"2026-07-05T09:25:36.152399Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:25:36.152399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"un6ShDToUJcu7Xe2hZB5yvKQp3uS3R9inFMNj9FEjStvqfSmNtQMkaUU8iwvf+Eh136lITMUjMfF+EpA+LusBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:25:36.152819Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.09336","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3352fc063c9d999347422db413baa70d5823a3a47c9ae196ac54648c353cc442","sha256:3b0677c92ae40c936e1501fb3a38f8ebae6d4b947027428ebf4cbf7c66c46680"],"state_sha256":"d6c10d6ee19cf5f8128387c57758b4a77128b1d331a224fda416c2371ca0038b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ctZrR+Ly2J6++FPZMFoTC1NboxWJaQYDeZMD7/b6erRsH/5/ImW35arwyz+1BxAyDq4bllvVGxXkBKAEkC53CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T07:08:22.898735Z","bundle_sha256":"a1c5db0b841d1dd9c1aee0ef1b52e776a628a25402eae3f566ef9a84a7fff2a2"}}