{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:A4NYKLDEZBTKOWGVMCD7MUGIC7","short_pith_number":"pith:A4NYKLDE","canonical_record":{"source":{"id":"2401.13986","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-25T07:04:30Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"35cea36cf2bc55d48f3a11ec00c686e96b1a919c9d511c99d9189d4e07a055dc","abstract_canon_sha256":"39fca422acb436f83be86f4f770049744970f07ce230d6a3222b7c61b0d57b40"},"schema_version":"1.0"},"canonical_sha256":"071b852c64c866a758d56087f650c817dda1420e877ba53699881fa85d36bde0","source":{"kind":"arxiv","id":"2401.13986","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.13986","created_at":"2026-07-05T07:37:20Z"},{"alias_kind":"arxiv_version","alias_value":"2401.13986v1","created_at":"2026-07-05T07:37:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.13986","created_at":"2026-07-05T07:37:20Z"},{"alias_kind":"pith_short_12","alias_value":"A4NYKLDEZBTK","created_at":"2026-07-05T07:37:20Z"},{"alias_kind":"pith_short_16","alias_value":"A4NYKLDEZBTKOWGV","created_at":"2026-07-05T07:37:20Z"},{"alias_kind":"pith_short_8","alias_value":"A4NYKLDE","created_at":"2026-07-05T07:37:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:A4NYKLDEZBTKOWGVMCD7MUGIC7","target":"record","payload":{"canonical_record":{"source":{"id":"2401.13986","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-25T07:04:30Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"35cea36cf2bc55d48f3a11ec00c686e96b1a919c9d511c99d9189d4e07a055dc","abstract_canon_sha256":"39fca422acb436f83be86f4f770049744970f07ce230d6a3222b7c61b0d57b40"},"schema_version":"1.0"},"canonical_sha256":"071b852c64c866a758d56087f650c817dda1420e877ba53699881fa85d36bde0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:37:20.408096Z","signature_b64":"utb6wjdq3DggOk9wnvUTTITEEgFCBAGTBpqOg9B3zaYW52cFqjHWeTsZfYzPXwB2Gfocg0D9wddDELoSMJnDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"071b852c64c866a758d56087f650c817dda1420e877ba53699881fa85d36bde0","last_reissued_at":"2026-07-05T07:37:20.407707Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:37:20.407707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.13986","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":"2026-07-05T07:37:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"554FBdcrPz1euVN/axzo/ZIp1Tm55fnfv+jl8AqJNB4MU83G3xXuf57QugiVlTytY7Pzn0zWSNS+rbvII4F7DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:26:01.086744Z"},"content_sha256":"a5acb1be44e36b2233c2cab8fe3cb6d4c15ffc872d5dd69ab19931dcf9e45487","schema_version":"1.0","event_id":"sha256:a5acb1be44e36b2233c2cab8fe3cb6d4c15ffc872d5dd69ab19931dcf9e45487"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:A4NYKLDEZBTKOWGVMCD7MUGIC7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Bin Yu, Chandan Singh, He He, Jianfeng Gao, Simiao Zuo, XiaoDong Liu, Yanda Chen","submitted_at":"2024-01-25T07:04:30Z","abstract_excerpt":"Large language models (LLMs) often generate convincing, fluent explanations. However, different from humans, they often generate inconsistent explanations on different inputs. For example, an LLM may generate the explanation \"all birds can fly\" when answering the question \"Can sparrows fly?\" but meanwhile answer \"no\" to the related question \"Can penguins fly?\". Explanations should be consistent across related examples so that they allow a human to simulate the LLM's decision process on multiple examples. We propose explanation-consistency finetuning (EC-finetuning), a method that adapts LLMs t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.13986","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/2401.13986/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":"2026-07-05T07:37:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HR6JJjqD9AHCVcpYNqBqlYNdVgEW1orLevRr17V/zUTftQT4mK2D/8jA21mbafkTU3LjATYHtYwr1SETBkmwDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:26:01.087659Z"},"content_sha256":"076332a212e580512ef02d638c0994856ad7ec055d50053dee06538f1404ab23","schema_version":"1.0","event_id":"sha256:076332a212e580512ef02d638c0994856ad7ec055d50053dee06538f1404ab23"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A4NYKLDEZBTKOWGVMCD7MUGIC7/bundle.json","state_url":"https://pith.science/pith/A4NYKLDEZBTKOWGVMCD7MUGIC7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A4NYKLDEZBTKOWGVMCD7MUGIC7/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":"2026-08-05T14:26:01Z","links":{"resolver":"https://pith.science/pith/A4NYKLDEZBTKOWGVMCD7MUGIC7","bundle":"https://pith.science/pith/A4NYKLDEZBTKOWGVMCD7MUGIC7/bundle.json","state":"https://pith.science/pith/A4NYKLDEZBTKOWGVMCD7MUGIC7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A4NYKLDEZBTKOWGVMCD7MUGIC7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:A4NYKLDEZBTKOWGVMCD7MUGIC7","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":"39fca422acb436f83be86f4f770049744970f07ce230d6a3222b7c61b0d57b40","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-25T07:04:30Z","title_canon_sha256":"35cea36cf2bc55d48f3a11ec00c686e96b1a919c9d511c99d9189d4e07a055dc"},"schema_version":"1.0","source":{"id":"2401.13986","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.13986","created_at":"2026-07-05T07:37:20Z"},{"alias_kind":"arxiv_version","alias_value":"2401.13986v1","created_at":"2026-07-05T07:37:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.13986","created_at":"2026-07-05T07:37:20Z"},{"alias_kind":"pith_short_12","alias_value":"A4NYKLDEZBTK","created_at":"2026-07-05T07:37:20Z"},{"alias_kind":"pith_short_16","alias_value":"A4NYKLDEZBTKOWGV","created_at":"2026-07-05T07:37:20Z"},{"alias_kind":"pith_short_8","alias_value":"A4NYKLDE","created_at":"2026-07-05T07:37:20Z"}],"graph_snapshots":[{"event_id":"sha256:076332a212e580512ef02d638c0994856ad7ec055d50053dee06538f1404ab23","target":"graph","created_at":"2026-07-05T07:37:20Z","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/2401.13986/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) often generate convincing, fluent explanations. However, different from humans, they often generate inconsistent explanations on different inputs. For example, an LLM may generate the explanation \"all birds can fly\" when answering the question \"Can sparrows fly?\" but meanwhile answer \"no\" to the related question \"Can penguins fly?\". Explanations should be consistent across related examples so that they allow a human to simulate the LLM's decision process on multiple examples. We propose explanation-consistency finetuning (EC-finetuning), a method that adapts LLMs t","authors_text":"Bin Yu, Chandan Singh, He He, Jianfeng Gao, Simiao Zuo, XiaoDong Liu, Yanda Chen","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-25T07:04:30Z","title":"Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.13986","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:a5acb1be44e36b2233c2cab8fe3cb6d4c15ffc872d5dd69ab19931dcf9e45487","target":"record","created_at":"2026-07-05T07:37:20Z","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":"39fca422acb436f83be86f4f770049744970f07ce230d6a3222b7c61b0d57b40","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-25T07:04:30Z","title_canon_sha256":"35cea36cf2bc55d48f3a11ec00c686e96b1a919c9d511c99d9189d4e07a055dc"},"schema_version":"1.0","source":{"id":"2401.13986","kind":"arxiv","version":1}},"canonical_sha256":"071b852c64c866a758d56087f650c817dda1420e877ba53699881fa85d36bde0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"071b852c64c866a758d56087f650c817dda1420e877ba53699881fa85d36bde0","first_computed_at":"2026-07-05T07:37:20.407707Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:37:20.407707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"utb6wjdq3DggOk9wnvUTTITEEgFCBAGTBpqOg9B3zaYW52cFqjHWeTsZfYzPXwB2Gfocg0D9wddDELoSMJnDCg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:37:20.408096Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.13986","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a5acb1be44e36b2233c2cab8fe3cb6d4c15ffc872d5dd69ab19931dcf9e45487","sha256:076332a212e580512ef02d638c0994856ad7ec055d50053dee06538f1404ab23"],"state_sha256":"5a1fa2d1d5eee90de29db12a7d4c95d71c5fe74a331f6791eef5b88bc48e10be"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ipqpruoslhGNuOMcqVhFCTgGZ58dFBG9+Q6RzNByHv829s1/nTLj5whvIV617ijbNa9TdWZpAyAsXOCjJnpnDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T14:26:01.128484Z","bundle_sha256":"cea77bb242b321582d1468a2feb8c453ed4bfc4f82bfd4fc47048aac5a8b7017"}}