{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:YPFAA6MGVFPETBJEY3D37UVZVZ","short_pith_number":"pith:YPFAA6MG","canonical_record":{"source":{"id":"2309.07120","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-09-13T17:57:21Z","cross_cats_sorted":["cs.AI","cs.CV","cs.CY","cs.LG"],"title_canon_sha256":"4f0acabdbc0f26b1f9ad239465e4bdca6ce0de82aef3f41e853b43b96c986b3d","abstract_canon_sha256":"59c4c41a9a38deee9b2f12dd44710407cdc75eea0ac8c7cb4855a292e9a04e69"},"schema_version":"1.0"},"canonical_sha256":"c3ca007986a95e498524c6c7bfd2b9ae43a25d81cd5f8af85e2bbc1bca245572","source":{"kind":"arxiv","id":"2309.07120","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.07120","created_at":"2026-07-05T06:50:26Z"},{"alias_kind":"arxiv_version","alias_value":"2309.07120v1","created_at":"2026-07-05T06:50:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.07120","created_at":"2026-07-05T06:50:26Z"},{"alias_kind":"pith_short_12","alias_value":"YPFAA6MGVFPE","created_at":"2026-07-05T06:50:26Z"},{"alias_kind":"pith_short_16","alias_value":"YPFAA6MGVFPETBJE","created_at":"2026-07-05T06:50:26Z"},{"alias_kind":"pith_short_8","alias_value":"YPFAA6MG","created_at":"2026-07-05T06:50:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:YPFAA6MGVFPETBJEY3D37UVZVZ","target":"record","payload":{"canonical_record":{"source":{"id":"2309.07120","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-09-13T17:57:21Z","cross_cats_sorted":["cs.AI","cs.CV","cs.CY","cs.LG"],"title_canon_sha256":"4f0acabdbc0f26b1f9ad239465e4bdca6ce0de82aef3f41e853b43b96c986b3d","abstract_canon_sha256":"59c4c41a9a38deee9b2f12dd44710407cdc75eea0ac8c7cb4855a292e9a04e69"},"schema_version":"1.0"},"canonical_sha256":"c3ca007986a95e498524c6c7bfd2b9ae43a25d81cd5f8af85e2bbc1bca245572","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:50:26.675827Z","signature_b64":"FfxxCo/ZevrVvPOO95kVvm8uWDOFQItIzHPW067DDFNbZvqs7u8yr8E1Koe9SDoPgh3XtPMMzM6hYauQWToXCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3ca007986a95e498524c6c7bfd2b9ae43a25d81cd5f8af85e2bbc1bca245572","last_reissued_at":"2026-07-05T06:50:26.675331Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:50:26.675331Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.07120","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":"2026-07-05T06:50:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YkgEBuHj3IUAaYb2JS9+ybAKcHGaLjJiy8kEF3HxxHYnywNUQln25ZArElQG3MRv90c5k2Wlr2wQqae/mtUIDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T21:03:42.860880Z"},"content_sha256":"b06a0966794e98098cd1928b7e80a7303e795f19017e5bcddfc63fd66bccdc44","schema_version":"1.0","event_id":"sha256:b06a0966794e98098cd1928b7e80a7303e795f19017e5bcddfc63fd66bccdc44"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:YPFAA6MGVFPETBJEY3D37UVZVZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and Ethics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.CY","cs.LG"],"primary_cat":"cs.CL","authors_text":"Bingchen Zhao, Chen Wei, Cihang Xie, Haoqin Tu","submitted_at":"2023-09-13T17:57:21Z","abstract_excerpt":"Multi-modal large language models (MLLMs) are trained based on large language models (LLM), with an enhanced capability to comprehend multi-modal inputs and generate textual responses. While they excel in multi-modal tasks, the pure NLP abilities of MLLMs are often underestimated and left untested. In this study, we get out of the box and unveil an intriguing characteristic of MLLMs -- our preliminary results suggest that visual instruction tuning, a prevailing strategy for transitioning LLMs into MLLMs, unexpectedly and interestingly helps models attain both improved truthfulness and ethical "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.07120","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/2309.07120/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":"2026-07-05T06:50:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PfBdJVJp1p4B0o2KZ62QAGhB+BUvlXpHRWlk4qHVcL35Lu3qv6qzDmYW2RD9oKYV17DaGaUa0hSMvgoThEpFAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T21:03:42.861262Z"},"content_sha256":"4c543cfe16c3afc3594e4995dddf3e4ccb29794b7ce0fa44a35e3068d3965fd5","schema_version":"1.0","event_id":"sha256:4c543cfe16c3afc3594e4995dddf3e4ccb29794b7ce0fa44a35e3068d3965fd5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YPFAA6MGVFPETBJEY3D37UVZVZ/bundle.json","state_url":"https://pith.science/pith/YPFAA6MGVFPETBJEY3D37UVZVZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YPFAA6MGVFPETBJEY3D37UVZVZ/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":"2026-08-06T21:03:42Z","links":{"resolver":"https://pith.science/pith/YPFAA6MGVFPETBJEY3D37UVZVZ","bundle":"https://pith.science/pith/YPFAA6MGVFPETBJEY3D37UVZVZ/bundle.json","state":"https://pith.science/pith/YPFAA6MGVFPETBJEY3D37UVZVZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YPFAA6MGVFPETBJEY3D37UVZVZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:YPFAA6MGVFPETBJEY3D37UVZVZ","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":"59c4c41a9a38deee9b2f12dd44710407cdc75eea0ac8c7cb4855a292e9a04e69","cross_cats_sorted":["cs.AI","cs.CV","cs.CY","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-09-13T17:57:21Z","title_canon_sha256":"4f0acabdbc0f26b1f9ad239465e4bdca6ce0de82aef3f41e853b43b96c986b3d"},"schema_version":"1.0","source":{"id":"2309.07120","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.07120","created_at":"2026-07-05T06:50:26Z"},{"alias_kind":"arxiv_version","alias_value":"2309.07120v1","created_at":"2026-07-05T06:50:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.07120","created_at":"2026-07-05T06:50:26Z"},{"alias_kind":"pith_short_12","alias_value":"YPFAA6MGVFPE","created_at":"2026-07-05T06:50:26Z"},{"alias_kind":"pith_short_16","alias_value":"YPFAA6MGVFPETBJE","created_at":"2026-07-05T06:50:26Z"},{"alias_kind":"pith_short_8","alias_value":"YPFAA6MG","created_at":"2026-07-05T06:50:26Z"}],"graph_snapshots":[{"event_id":"sha256:4c543cfe16c3afc3594e4995dddf3e4ccb29794b7ce0fa44a35e3068d3965fd5","target":"graph","created_at":"2026-07-05T06:50:26Z","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/2309.07120/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-modal large language models (MLLMs) are trained based on large language models (LLM), with an enhanced capability to comprehend multi-modal inputs and generate textual responses. While they excel in multi-modal tasks, the pure NLP abilities of MLLMs are often underestimated and left untested. In this study, we get out of the box and unveil an intriguing characteristic of MLLMs -- our preliminary results suggest that visual instruction tuning, a prevailing strategy for transitioning LLMs into MLLMs, unexpectedly and interestingly helps models attain both improved truthfulness and ethical ","authors_text":"Bingchen Zhao, Chen Wei, Cihang Xie, Haoqin Tu","cross_cats":["cs.AI","cs.CV","cs.CY","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-09-13T17:57:21Z","title":"Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and Ethics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.07120","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:b06a0966794e98098cd1928b7e80a7303e795f19017e5bcddfc63fd66bccdc44","target":"record","created_at":"2026-07-05T06:50:26Z","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":"59c4c41a9a38deee9b2f12dd44710407cdc75eea0ac8c7cb4855a292e9a04e69","cross_cats_sorted":["cs.AI","cs.CV","cs.CY","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-09-13T17:57:21Z","title_canon_sha256":"4f0acabdbc0f26b1f9ad239465e4bdca6ce0de82aef3f41e853b43b96c986b3d"},"schema_version":"1.0","source":{"id":"2309.07120","kind":"arxiv","version":1}},"canonical_sha256":"c3ca007986a95e498524c6c7bfd2b9ae43a25d81cd5f8af85e2bbc1bca245572","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c3ca007986a95e498524c6c7bfd2b9ae43a25d81cd5f8af85e2bbc1bca245572","first_computed_at":"2026-07-05T06:50:26.675331Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:50:26.675331Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FfxxCo/ZevrVvPOO95kVvm8uWDOFQItIzHPW067DDFNbZvqs7u8yr8E1Koe9SDoPgh3XtPMMzM6hYauQWToXCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:50:26.675827Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.07120","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b06a0966794e98098cd1928b7e80a7303e795f19017e5bcddfc63fd66bccdc44","sha256:4c543cfe16c3afc3594e4995dddf3e4ccb29794b7ce0fa44a35e3068d3965fd5"],"state_sha256":"99114dd1e792c0320a9dad7ef712d84bb55aa771d7065c91808913f8ddb9a648"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bnjZz27SWoXlZox4+ZiP0aGmcQieZ9jzhaWwd/nOBGGgv1+pCfFzPwN+5CLxvgbe99enb52e01uBmNwGMZSfCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T21:03:42.864023Z","bundle_sha256":"779b4ca8d7668a08d06288f92d4156006c0c50e880af3f3ef8b28ff2eab812c9"}}