{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:FQB46EX4XB7VDE5HNUR7GQQX5C","short_pith_number":"pith:FQB46EX4","canonical_record":{"source":{"id":"2407.02397","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-02T16:15:01Z","cross_cats_sorted":[],"title_canon_sha256":"d93c9de7aebcd7f6237537f6543c873d3c1c7035861a4425580543d2ed937821","abstract_canon_sha256":"84001bf36a11561a4cf09014d123b06a5485d22a5868d7dec99d9fe16142798a"},"schema_version":"1.0"},"canonical_sha256":"2c03cf12fcb87f5193a76d23f34217e891909286b1cfdd67969e3ef3a03602cf","source":{"kind":"arxiv","id":"2407.02397","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.02397","created_at":"2026-07-05T11:24:04Z"},{"alias_kind":"arxiv_version","alias_value":"2407.02397v3","created_at":"2026-07-05T11:24:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.02397","created_at":"2026-07-05T11:24:04Z"},{"alias_kind":"pith_short_12","alias_value":"FQB46EX4XB7V","created_at":"2026-07-05T11:24:04Z"},{"alias_kind":"pith_short_16","alias_value":"FQB46EX4XB7VDE5H","created_at":"2026-07-05T11:24:04Z"},{"alias_kind":"pith_short_8","alias_value":"FQB46EX4","created_at":"2026-07-05T11:24:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:FQB46EX4XB7VDE5HNUR7GQQX5C","target":"record","payload":{"canonical_record":{"source":{"id":"2407.02397","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-02T16:15:01Z","cross_cats_sorted":[],"title_canon_sha256":"d93c9de7aebcd7f6237537f6543c873d3c1c7035861a4425580543d2ed937821","abstract_canon_sha256":"84001bf36a11561a4cf09014d123b06a5485d22a5868d7dec99d9fe16142798a"},"schema_version":"1.0"},"canonical_sha256":"2c03cf12fcb87f5193a76d23f34217e891909286b1cfdd67969e3ef3a03602cf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:24:04.872753Z","signature_b64":"bJL7IzXXQnn/yH/0ly0JCmxewlSGXaxULWm2SNe2Q+xBtAHb6KA64Y5KqSG3psHswkvcsbDbeusb0p6KH4t5Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c03cf12fcb87f5193a76d23f34217e891909286b1cfdd67969e3ef3a03602cf","last_reissued_at":"2026-07-05T11:24:04.872237Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:24:04.872237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.02397","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:24:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EloCUNBB+plw4D8/ZNW3QIiXawP+/9rkUSebwEoKhWyylHjY19l+UZNzmCeL8VTmrI4mDif30MfzGgXyqJjAAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T23:31:25.460020Z"},"content_sha256":"b10dc08ef72787d8ed170f8d047db6afde6d08a93e530e91e16b9c2c39e022ff","schema_version":"1.0","event_id":"sha256:b10dc08ef72787d8ed170f8d047db6afde6d08a93e530e91e16b9c2c39e022ff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:FQB46EX4XB7VDE5HNUR7GQQX5C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning to Refine with Fine-Grained Natural Language Feedback","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Greg Durrett, Junyi Jessy Li, Manya Wadhwa, Xinyu Zhao","submitted_at":"2024-07-02T16:15:01Z","abstract_excerpt":"Recent work has explored the capability of large language models (LLMs) to identify and correct errors in LLM-generated responses. These refinement approaches frequently evaluate what sizes of models are able to do refinement for what problems, but less attention is paid to what effective feedback for refinement looks like. In this work, we propose looking at refinement with feedback as a composition of three distinct LLM competencies: (1) detection of bad generations; (2) fine-grained natural language critique generation; (3) refining with fine-grained feedback. The first step can be implemen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.02397","kind":"arxiv","version":3},"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/2407.02397/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":"2026-07-05T11:24:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JrY6UFrf72qdCx4N4MBio/C7Dc3XEreRnAb053t/95KSTQztT29z+pzPQSzaUnxSA+A3ZYTpiyqfm0+JHIP4Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T23:31:25.460675Z"},"content_sha256":"f498795ffd21af4b557dfc3999b64ab40ead30be5068b396f3d91ba43f1586b1","schema_version":"1.0","event_id":"sha256:f498795ffd21af4b557dfc3999b64ab40ead30be5068b396f3d91ba43f1586b1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FQB46EX4XB7VDE5HNUR7GQQX5C/bundle.json","state_url":"https://pith.science/pith/FQB46EX4XB7VDE5HNUR7GQQX5C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FQB46EX4XB7VDE5HNUR7GQQX5C/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":"2026-08-10T23:31:25Z","links":{"resolver":"https://pith.science/pith/FQB46EX4XB7VDE5HNUR7GQQX5C","bundle":"https://pith.science/pith/FQB46EX4XB7VDE5HNUR7GQQX5C/bundle.json","state":"https://pith.science/pith/FQB46EX4XB7VDE5HNUR7GQQX5C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FQB46EX4XB7VDE5HNUR7GQQX5C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:FQB46EX4XB7VDE5HNUR7GQQX5C","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":"84001bf36a11561a4cf09014d123b06a5485d22a5868d7dec99d9fe16142798a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-02T16:15:01Z","title_canon_sha256":"d93c9de7aebcd7f6237537f6543c873d3c1c7035861a4425580543d2ed937821"},"schema_version":"1.0","source":{"id":"2407.02397","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.02397","created_at":"2026-07-05T11:24:04Z"},{"alias_kind":"arxiv_version","alias_value":"2407.02397v3","created_at":"2026-07-05T11:24:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.02397","created_at":"2026-07-05T11:24:04Z"},{"alias_kind":"pith_short_12","alias_value":"FQB46EX4XB7V","created_at":"2026-07-05T11:24:04Z"},{"alias_kind":"pith_short_16","alias_value":"FQB46EX4XB7VDE5H","created_at":"2026-07-05T11:24:04Z"},{"alias_kind":"pith_short_8","alias_value":"FQB46EX4","created_at":"2026-07-05T11:24:04Z"}],"graph_snapshots":[{"event_id":"sha256:f498795ffd21af4b557dfc3999b64ab40ead30be5068b396f3d91ba43f1586b1","target":"graph","created_at":"2026-07-05T11:24:04Z","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/2407.02397/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent work has explored the capability of large language models (LLMs) to identify and correct errors in LLM-generated responses. These refinement approaches frequently evaluate what sizes of models are able to do refinement for what problems, but less attention is paid to what effective feedback for refinement looks like. In this work, we propose looking at refinement with feedback as a composition of three distinct LLM competencies: (1) detection of bad generations; (2) fine-grained natural language critique generation; (3) refining with fine-grained feedback. The first step can be implemen","authors_text":"Greg Durrett, Junyi Jessy Li, Manya Wadhwa, Xinyu Zhao","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-02T16:15:01Z","title":"Learning to Refine with Fine-Grained Natural Language Feedback"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.02397","kind":"arxiv","version":3},"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:b10dc08ef72787d8ed170f8d047db6afde6d08a93e530e91e16b9c2c39e022ff","target":"record","created_at":"2026-07-05T11:24:04Z","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":"84001bf36a11561a4cf09014d123b06a5485d22a5868d7dec99d9fe16142798a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-02T16:15:01Z","title_canon_sha256":"d93c9de7aebcd7f6237537f6543c873d3c1c7035861a4425580543d2ed937821"},"schema_version":"1.0","source":{"id":"2407.02397","kind":"arxiv","version":3}},"canonical_sha256":"2c03cf12fcb87f5193a76d23f34217e891909286b1cfdd67969e3ef3a03602cf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2c03cf12fcb87f5193a76d23f34217e891909286b1cfdd67969e3ef3a03602cf","first_computed_at":"2026-07-05T11:24:04.872237Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:24:04.872237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bJL7IzXXQnn/yH/0ly0JCmxewlSGXaxULWm2SNe2Q+xBtAHb6KA64Y5KqSG3psHswkvcsbDbeusb0p6KH4t5Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:24:04.872753Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.02397","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b10dc08ef72787d8ed170f8d047db6afde6d08a93e530e91e16b9c2c39e022ff","sha256:f498795ffd21af4b557dfc3999b64ab40ead30be5068b396f3d91ba43f1586b1"],"state_sha256":"2faedc334f0355b1c8f8d56a581a3e12a167812ddb1f0798b077e2976764dd13"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ah72Tzbqm8711Z19Gv/m5EFvg2Cu93M3JcdFVCs7KcmbW6ufjVxnfDpkKtdZQ0o1WXk4ypVcsIw6RwUaph0KAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T23:31:25.464980Z","bundle_sha256":"77684f7bad3eb9086eff59bac93af589148350277a99d41f5be0ca497ef38284"}}