{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:K5GYBH5QVJ562GCB2GEU7MAUT6","short_pith_number":"pith:K5GYBH5Q","canonical_record":{"source":{"id":"2310.11716","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-18T05:13:47Z","cross_cats_sorted":[],"title_canon_sha256":"a3f3a7b8678d1fa65e1ac62664dbcfc476ca9b688badaa71be19b47f2bbd78f6","abstract_canon_sha256":"511b9d97c77f78f983694b8604fbcd69ea687ef0bf15e47a597ad8fe0d8a2401"},"schema_version":"1.0"},"canonical_sha256":"574d809fb0aa7bed1841d1894fb0149fa1dcd6f68dd03c30a51b1aea5503ed10","source":{"kind":"arxiv","id":"2310.11716","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.11716","created_at":"2026-07-05T07:02:11Z"},{"alias_kind":"arxiv_version","alias_value":"2310.11716v1","created_at":"2026-07-05T07:02:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.11716","created_at":"2026-07-05T07:02:11Z"},{"alias_kind":"pith_short_12","alias_value":"K5GYBH5QVJ56","created_at":"2026-07-05T07:02:11Z"},{"alias_kind":"pith_short_16","alias_value":"K5GYBH5QVJ562GCB","created_at":"2026-07-05T07:02:11Z"},{"alias_kind":"pith_short_8","alias_value":"K5GYBH5Q","created_at":"2026-07-05T07:02:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:K5GYBH5QVJ562GCB2GEU7MAUT6","target":"record","payload":{"canonical_record":{"source":{"id":"2310.11716","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-18T05:13:47Z","cross_cats_sorted":[],"title_canon_sha256":"a3f3a7b8678d1fa65e1ac62664dbcfc476ca9b688badaa71be19b47f2bbd78f6","abstract_canon_sha256":"511b9d97c77f78f983694b8604fbcd69ea687ef0bf15e47a597ad8fe0d8a2401"},"schema_version":"1.0"},"canonical_sha256":"574d809fb0aa7bed1841d1894fb0149fa1dcd6f68dd03c30a51b1aea5503ed10","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:02:11.309827Z","signature_b64":"LzL3xp1lu6X2TiWpphYd9lyjXyulsdU3umwCCnHsjjhmz1mNBldTbpS67QH1bJzg3qUBBiBA4w8oOZWcirdbCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"574d809fb0aa7bed1841d1894fb0149fa1dcd6f68dd03c30a51b1aea5503ed10","last_reissued_at":"2026-07-05T07:02:11.309451Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:02:11.309451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.11716","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:02:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YMlo828qNxBOq4qUETqg+hUEKIxRe6dWs+9m/y9jkthX1oLzBiI9xuhrDoGApxuLnaXwlJvqOXTHfuFVQPvdBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T19:42:14.701760Z"},"content_sha256":"2db237b269c61b72e6ff99855b94c9c4c7aae30d71cbb32811d5b74aca149cff","schema_version":"1.0","event_id":"sha256:2db237b269c61b72e6ff99855b94c9c4c7aae30d71cbb32811d5b74aca149cff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:K5GYBH5QVJ562GCB2GEU7MAUT6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Heng Huang, Jiuhai Chen, Jiuxiang Gu, Lichang Chen, Ming Li, Shwai He, Tianyi Zhou","submitted_at":"2023-10-18T05:13:47Z","abstract_excerpt":"Recent advancements in Large Language Models (LLMs) have expanded the horizons of natural language understanding and generation. Notably, the output control and alignment with the input of LLMs can be refined through instruction tuning. However, as highlighted in several studies, low-quality data in the training set are usually detrimental to instruction tuning, resulting in inconsistent or even misleading LLM outputs. We propose a novel method, termed \"reflection-tuning,\" which addresses the problem by self-improvement and judging capabilities of LLMs. This approach utilizes an oracle LLM to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.11716","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/2310.11716/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":"2026-07-05T07:02:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WlsMJL2zeRNyANJOIc2kRPlpRi9NrnTUaoGkSQMI+POYuZ4mO0Numjrji/KC0fq9hibdZrCBh+/v8dDtp716Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T19:42:14.702318Z"},"content_sha256":"9dab175b7393c1956806a372cd5e2d4830073e47e15d07d8007fb935057e0853","schema_version":"1.0","event_id":"sha256:9dab175b7393c1956806a372cd5e2d4830073e47e15d07d8007fb935057e0853"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K5GYBH5QVJ562GCB2GEU7MAUT6/bundle.json","state_url":"https://pith.science/pith/K5GYBH5QVJ562GCB2GEU7MAUT6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K5GYBH5QVJ562GCB2GEU7MAUT6/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":"2026-08-14T19:42:14Z","links":{"resolver":"https://pith.science/pith/K5GYBH5QVJ562GCB2GEU7MAUT6","bundle":"https://pith.science/pith/K5GYBH5QVJ562GCB2GEU7MAUT6/bundle.json","state":"https://pith.science/pith/K5GYBH5QVJ562GCB2GEU7MAUT6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K5GYBH5QVJ562GCB2GEU7MAUT6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:K5GYBH5QVJ562GCB2GEU7MAUT6","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":"511b9d97c77f78f983694b8604fbcd69ea687ef0bf15e47a597ad8fe0d8a2401","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-18T05:13:47Z","title_canon_sha256":"a3f3a7b8678d1fa65e1ac62664dbcfc476ca9b688badaa71be19b47f2bbd78f6"},"schema_version":"1.0","source":{"id":"2310.11716","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.11716","created_at":"2026-07-05T07:02:11Z"},{"alias_kind":"arxiv_version","alias_value":"2310.11716v1","created_at":"2026-07-05T07:02:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.11716","created_at":"2026-07-05T07:02:11Z"},{"alias_kind":"pith_short_12","alias_value":"K5GYBH5QVJ56","created_at":"2026-07-05T07:02:11Z"},{"alias_kind":"pith_short_16","alias_value":"K5GYBH5QVJ562GCB","created_at":"2026-07-05T07:02:11Z"},{"alias_kind":"pith_short_8","alias_value":"K5GYBH5Q","created_at":"2026-07-05T07:02:11Z"}],"graph_snapshots":[{"event_id":"sha256:9dab175b7393c1956806a372cd5e2d4830073e47e15d07d8007fb935057e0853","target":"graph","created_at":"2026-07-05T07:02:11Z","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/2310.11716/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advancements in Large Language Models (LLMs) have expanded the horizons of natural language understanding and generation. Notably, the output control and alignment with the input of LLMs can be refined through instruction tuning. However, as highlighted in several studies, low-quality data in the training set are usually detrimental to instruction tuning, resulting in inconsistent or even misleading LLM outputs. We propose a novel method, termed \"reflection-tuning,\" which addresses the problem by self-improvement and judging capabilities of LLMs. This approach utilizes an oracle LLM to ","authors_text":"Heng Huang, Jiuhai Chen, Jiuxiang Gu, Lichang Chen, Ming Li, Shwai He, Tianyi Zhou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-18T05:13:47Z","title":"Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.11716","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":"sha256:2db237b269c61b72e6ff99855b94c9c4c7aae30d71cbb32811d5b74aca149cff","target":"record","created_at":"2026-07-05T07:02:11Z","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":"511b9d97c77f78f983694b8604fbcd69ea687ef0bf15e47a597ad8fe0d8a2401","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-18T05:13:47Z","title_canon_sha256":"a3f3a7b8678d1fa65e1ac62664dbcfc476ca9b688badaa71be19b47f2bbd78f6"},"schema_version":"1.0","source":{"id":"2310.11716","kind":"arxiv","version":1}},"canonical_sha256":"574d809fb0aa7bed1841d1894fb0149fa1dcd6f68dd03c30a51b1aea5503ed10","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"574d809fb0aa7bed1841d1894fb0149fa1dcd6f68dd03c30a51b1aea5503ed10","first_computed_at":"2026-07-05T07:02:11.309451Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:02:11.309451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LzL3xp1lu6X2TiWpphYd9lyjXyulsdU3umwCCnHsjjhmz1mNBldTbpS67QH1bJzg3qUBBiBA4w8oOZWcirdbCw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:02:11.309827Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.11716","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2db237b269c61b72e6ff99855b94c9c4c7aae30d71cbb32811d5b74aca149cff","sha256:9dab175b7393c1956806a372cd5e2d4830073e47e15d07d8007fb935057e0853"],"state_sha256":"7b149c3150eafd616c64927f32a467581897a282b06f0c5dac1023dacb473378"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MCjKboe1Gs7IfIkq/lxwOgAC5UiU9QhAk03g1+X3Aj0DKkPSZL9Jm4DUNlEcJZO5j+iea7n9YxE5fIVpKeqcBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T19:42:14.707350Z","bundle_sha256":"f4d0198834927bbe7facc4fe51c5ded2242e0b33f37c38ecfddcdd07df8c677d"}}