{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:ZGZYN5KFZRC4DTYMACJ3XVYVV4","short_pith_number":"pith:ZGZYN5KF","canonical_record":{"source":{"id":"2505.23410","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-29T12:59:30Z","cross_cats_sorted":[],"title_canon_sha256":"9e8ccf9a863e6fe9d0a7a565f1672d09c70d50da9382d1a7aff2cde42155e470","abstract_canon_sha256":"1f428ca72891d9aeee92fa4988a93b6859ecb864a1af8c6d56236cbcbe33b774"},"schema_version":"1.0"},"canonical_sha256":"c9b386f545cc45c1cf0c0093bbd715af2844106460463f8d3a35ec07a1e3efd5","source":{"kind":"arxiv","id":"2505.23410","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.23410","created_at":"2026-07-05T11:12:01Z"},{"alias_kind":"arxiv_version","alias_value":"2505.23410v1","created_at":"2026-07-05T11:12:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.23410","created_at":"2026-07-05T11:12:01Z"},{"alias_kind":"pith_short_12","alias_value":"ZGZYN5KFZRC4","created_at":"2026-07-05T11:12:01Z"},{"alias_kind":"pith_short_16","alias_value":"ZGZYN5KFZRC4DTYM","created_at":"2026-07-05T11:12:01Z"},{"alias_kind":"pith_short_8","alias_value":"ZGZYN5KF","created_at":"2026-07-05T11:12:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:ZGZYN5KFZRC4DTYMACJ3XVYVV4","target":"record","payload":{"canonical_record":{"source":{"id":"2505.23410","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-29T12:59:30Z","cross_cats_sorted":[],"title_canon_sha256":"9e8ccf9a863e6fe9d0a7a565f1672d09c70d50da9382d1a7aff2cde42155e470","abstract_canon_sha256":"1f428ca72891d9aeee92fa4988a93b6859ecb864a1af8c6d56236cbcbe33b774"},"schema_version":"1.0"},"canonical_sha256":"c9b386f545cc45c1cf0c0093bbd715af2844106460463f8d3a35ec07a1e3efd5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:12:01.268822Z","signature_b64":"rcttB71esEUSZ7c/vF5oLBtn+ACUzwEq7fZC2NOkrme+k2yHNFbH/ulRkQCKMBvZgaCQCLCDEAttI5jaFiiICQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9b386f545cc45c1cf0c0093bbd715af2844106460463f8d3a35ec07a1e3efd5","last_reissued_at":"2026-07-05T11:12:01.268335Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:12:01.268335Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.23410","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":"2026-07-05T11:12:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3XkWs0cn5ylv5qfDlgZ0253aSVcmf2O2J3mvwUHjJierTWH91t4Y+EzTQIAPzSZsuiq5ja6qpG0nxBq9rJrnCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:48:35.573086Z"},"content_sha256":"a4a1b98c42e2c594ccbbe0cd69def4d54d336819bd80e58728f9b49b3fdc2409","schema_version":"1.0","event_id":"sha256:a4a1b98c42e2c594ccbbe0cd69def4d54d336819bd80e58728f9b49b3fdc2409"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:ZGZYN5KFZRC4DTYMACJ3XVYVV4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hanbo Huang, Shiyu Liang, Xuan Gong","submitted_at":"2025-05-29T12:59:30Z","abstract_excerpt":"Factual knowledge extraction aims to explicitly extract knowledge parameterized in pre-trained language models for application in downstream tasks. While prior work has been investigating the impact of supervised fine-tuning data on the factuality of large language models (LLMs), its mechanism remains poorly understood. We revisit this impact through systematic experiments, with a particular focus on the factuality gap that arises when fine-tuning on known versus unknown knowledge. Our findings show that this gap can be mitigated at the inference stage, either under out-of-distribution (OOD) s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.23410","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/2505.23410/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":"2026-07-05T11:12:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/Sxr0cQgCEp7/7mLOnRH2LASoLW7NQaj/Fkybv+37aH/6sj7py1B2vYV/hnFFoaxjez5ken7M0q/9MRoHLxEDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:48:35.573599Z"},"content_sha256":"c8697fafbe092d5919d63306ab08bd5b2fb083efb5746fe2fb7ff5f0cc29fa73","schema_version":"1.0","event_id":"sha256:c8697fafbe092d5919d63306ab08bd5b2fb083efb5746fe2fb7ff5f0cc29fa73"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZGZYN5KFZRC4DTYMACJ3XVYVV4/bundle.json","state_url":"https://pith.science/pith/ZGZYN5KFZRC4DTYMACJ3XVYVV4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZGZYN5KFZRC4DTYMACJ3XVYVV4/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":"2026-08-07T23:48:35Z","links":{"resolver":"https://pith.science/pith/ZGZYN5KFZRC4DTYMACJ3XVYVV4","bundle":"https://pith.science/pith/ZGZYN5KFZRC4DTYMACJ3XVYVV4/bundle.json","state":"https://pith.science/pith/ZGZYN5KFZRC4DTYMACJ3XVYVV4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZGZYN5KFZRC4DTYMACJ3XVYVV4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ZGZYN5KFZRC4DTYMACJ3XVYVV4","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":"1f428ca72891d9aeee92fa4988a93b6859ecb864a1af8c6d56236cbcbe33b774","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-29T12:59:30Z","title_canon_sha256":"9e8ccf9a863e6fe9d0a7a565f1672d09c70d50da9382d1a7aff2cde42155e470"},"schema_version":"1.0","source":{"id":"2505.23410","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.23410","created_at":"2026-07-05T11:12:01Z"},{"alias_kind":"arxiv_version","alias_value":"2505.23410v1","created_at":"2026-07-05T11:12:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.23410","created_at":"2026-07-05T11:12:01Z"},{"alias_kind":"pith_short_12","alias_value":"ZGZYN5KFZRC4","created_at":"2026-07-05T11:12:01Z"},{"alias_kind":"pith_short_16","alias_value":"ZGZYN5KFZRC4DTYM","created_at":"2026-07-05T11:12:01Z"},{"alias_kind":"pith_short_8","alias_value":"ZGZYN5KF","created_at":"2026-07-05T11:12:01Z"}],"graph_snapshots":[{"event_id":"sha256:c8697fafbe092d5919d63306ab08bd5b2fb083efb5746fe2fb7ff5f0cc29fa73","target":"graph","created_at":"2026-07-05T11:12:01Z","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/2505.23410/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Factual knowledge extraction aims to explicitly extract knowledge parameterized in pre-trained language models for application in downstream tasks. While prior work has been investigating the impact of supervised fine-tuning data on the factuality of large language models (LLMs), its mechanism remains poorly understood. We revisit this impact through systematic experiments, with a particular focus on the factuality gap that arises when fine-tuning on known versus unknown knowledge. Our findings show that this gap can be mitigated at the inference stage, either under out-of-distribution (OOD) s","authors_text":"Hanbo Huang, Shiyu Liang, Xuan Gong","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-29T12:59:30Z","title":"From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.23410","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:a4a1b98c42e2c594ccbbe0cd69def4d54d336819bd80e58728f9b49b3fdc2409","target":"record","created_at":"2026-07-05T11:12:01Z","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":"1f428ca72891d9aeee92fa4988a93b6859ecb864a1af8c6d56236cbcbe33b774","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-05-29T12:59:30Z","title_canon_sha256":"9e8ccf9a863e6fe9d0a7a565f1672d09c70d50da9382d1a7aff2cde42155e470"},"schema_version":"1.0","source":{"id":"2505.23410","kind":"arxiv","version":1}},"canonical_sha256":"c9b386f545cc45c1cf0c0093bbd715af2844106460463f8d3a35ec07a1e3efd5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c9b386f545cc45c1cf0c0093bbd715af2844106460463f8d3a35ec07a1e3efd5","first_computed_at":"2026-07-05T11:12:01.268335Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:12:01.268335Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rcttB71esEUSZ7c/vF5oLBtn+ACUzwEq7fZC2NOkrme+k2yHNFbH/ulRkQCKMBvZgaCQCLCDEAttI5jaFiiICQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:12:01.268822Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.23410","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a4a1b98c42e2c594ccbbe0cd69def4d54d336819bd80e58728f9b49b3fdc2409","sha256:c8697fafbe092d5919d63306ab08bd5b2fb083efb5746fe2fb7ff5f0cc29fa73"],"state_sha256":"40ba07360ed249b9dea7dd057ba354cfd14a1d7f3a16c1b186e259d136f023fe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"23laS8XDeVg9ZzrI1UDH1HEyCXKQneYLgUhBT904hwNJ+9HW0Y+iiy2M5yFylgMq1RNXYbs70GTyd77ueSEFBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T23:48:35.578976Z","bundle_sha256":"e357a28d6f68d6708019126e6b6ed39904365e250ebab1da1ede7e1008106de7"}}