{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:MCYNG54FMM74BLASM5OMVQKGDV","short_pith_number":"pith:MCYNG54F","canonical_record":{"source":{"id":"2402.00253","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-02-01T00:33:21Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"33a9a211515ced3e215194accef8df66483c182165a90f379bf8d027480135ce","abstract_canon_sha256":"58e98477621291f8569dc0c279bd252a0b496b574d21a26e9188f320d8d2a5b9"},"schema_version":"1.0"},"canonical_sha256":"60b0d37785633fc0ac12675ccac1461d5c6257f36b2a5a10468ee6d6560931d5","source":{"kind":"arxiv","id":"2402.00253","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.00253","created_at":"2026-05-18T04:33:37Z"},{"alias_kind":"arxiv_version","alias_value":"2402.00253v2","created_at":"2026-05-18T04:33:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.00253","created_at":"2026-05-18T04:33:37Z"},{"alias_kind":"pith_short_12","alias_value":"MCYNG54FMM74","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"MCYNG54FMM74BLAS","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"MCYNG54F","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:MCYNG54FMM74BLASM5OMVQKGDV","target":"record","payload":{"canonical_record":{"source":{"id":"2402.00253","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-02-01T00:33:21Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"33a9a211515ced3e215194accef8df66483c182165a90f379bf8d027480135ce","abstract_canon_sha256":"58e98477621291f8569dc0c279bd252a0b496b574d21a26e9188f320d8d2a5b9"},"schema_version":"1.0"},"canonical_sha256":"60b0d37785633fc0ac12675ccac1461d5c6257f36b2a5a10468ee6d6560931d5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:33:37.171109Z","signature_b64":"pwJmCzGfSskX+ZdElXj8nnHLsPO3sNJ5z3nBys0FjZ1BDUOfY4RRYPalzt4vZoT5st621ASAjDvsBJ71iXRZAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60b0d37785633fc0ac12675ccac1461d5c6257f36b2a5a10468ee6d6560931d5","last_reissued_at":"2026-05-18T04:33:37.170404Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:33:37.170404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.00253","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T04:33:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dynXNInxgOv2YYYZVezvfx/kwo01p93c0yUwL2SVKC6BPGYUmoMC/anVdlhqNa6pQwm1Gq0DD4eoN0JkwhQQAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:35:53.100950Z"},"content_sha256":"0de41fec6929156439689bc3471b6e16cc7b856a9754c5a94d156de5a696080c","schema_version":"1.0","event_id":"sha256:0de41fec6929156439689bc3471b6e16cc7b856a9754c5a94d156de5a696080c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:MCYNG54FMM74BLASM5OMVQKGDV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Survey on Hallucination in Large Vision-Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Large vision-language models generate text that conflicts with input images, and this survey defines the problem while reviewing its symptoms, benchmarks, causes, and fixes.","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.CV","authors_text":"Dapeng Chen, Hanchao Liu, Ke Wang, Liping Hou, Rongjun Li, Wei Peng, Wenyuan Xue, Xiutian Zhao, Yifei Chen","submitted_at":"2024-02-01T00:33:21Z","abstract_excerpt":"Recent development of Large Vision-Language Models (LVLMs) has attracted growing attention within the AI landscape for its practical implementation potential. However, ``hallucination'', or more specifically, the misalignment between factual visual content and corresponding textual generation, poses a significant challenge of utilizing LVLMs. In this comprehensive survey, we dissect LVLM-related hallucinations in an attempt to establish an overview and facilitate future mitigation. Our scrutiny starts with a clarification of the concept of hallucinations in LVLMs, presenting a variety of hallu"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Hallucination, defined as the misalignment between factual visual content and corresponding textual generation, poses a significant challenge of utilizing LVLMs, and this survey establishes an overview to facilitate future mitigation.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That hallucinations can be consistently defined and isolated across diverse LVLM architectures and tasks without significant overlap with other error types such as reasoning failures.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"This survey reviews the definition, symptoms, evaluation benchmarks, root causes, and mitigation methods for hallucinations in large vision-language models.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Large vision-language models generate text that conflicts with input images, and this survey defines the problem while reviewing its symptoms, benchmarks, causes, and fixes.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"94927963cf9c0a8d68a781ebc484168fa21c4d54e9d5a0bf6136a10c04d26c83"},"source":{"id":"2402.00253","kind":"arxiv","version":2},"verdict":{"id":"bdbc8482-dc4c-481d-a809-04ade0131e21","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-13T22:05:38.517168Z","strongest_claim":"Hallucination, defined as the misalignment between factual visual content and corresponding textual generation, poses a significant challenge of utilizing LVLMs, and this survey establishes an overview to facilitate future mitigation.","one_line_summary":"This survey reviews the definition, symptoms, evaluation benchmarks, root causes, and mitigation methods for hallucinations in large vision-language models.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That hallucinations can be consistently defined and isolated across diverse LVLM architectures and tasks without significant overlap with other error types such as reasoning failures.","pith_extraction_headline":"Large vision-language models generate text that conflicts with input images, and this survey defines the problem while reviewing its symptoms, benchmarks, causes, and fixes."},"references":{"count":56,"sample":[{"doi":"","year":2022,"title":"Flamingo: a visual language model for few-shot learning","work_id":"994f306f-a295-42db-934f-7f15ed59e399","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","work_id":"cbc2bb21-b6bb-46c0-80bf-107e195ffe10","ref_index":2,"cited_arxiv_id":"2308.12966","is_internal_anchor":true},{"doi":"","year":null,"title":"Position-enhanced visual instruction tuning for multimodal large language models","work_id":"70a11f00-50ba-409c-b9da-619abaed18ea","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning","work_id":"fb62cd1b-3991-40be-a987-3cfa5772b5b5","ref_index":4,"cited_arxiv_id":"2310.09478","is_internal_anchor":true},{"doi":"","year":null,"title":"InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks","work_id":"d9e035c7-9e23-4cc2-ad3e-be080fbbf2d9","ref_index":5,"cited_arxiv_id":"2312.14238","is_internal_anchor":true}],"resolved_work":56,"snapshot_sha256":"7ba873e9d79fbe63fdd9e6941ac8119a72ac6b9a9e74ca44ed6137c5a6884015","internal_anchors":21},"formal_canon":{"evidence_count":2,"snapshot_sha256":"bfa4b190e5b5a0a408b1642f593f179644c4f57adb77771a6c7811986ef8d003"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"bdbc8482-dc4c-481d-a809-04ade0131e21"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T04:33:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AJF+mWIiCjr0NjrZMgTxvPI7xpTAD30QPgsEJReWRc85JKTKh1t0aBnhXds1ZE5J9x2LucsqEkxMKowOChUkAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:35:53.102033Z"},"content_sha256":"78f62bafac29962b71ece0688b17b18966b897b9d113a548378c6caca347f962","schema_version":"1.0","event_id":"sha256:78f62bafac29962b71ece0688b17b18966b897b9d113a548378c6caca347f962"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MCYNG54FMM74BLASM5OMVQKGDV/bundle.json","state_url":"https://pith.science/pith/MCYNG54FMM74BLASM5OMVQKGDV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MCYNG54FMM74BLASM5OMVQKGDV/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":"2026-05-26T02:35:53Z","links":{"resolver":"https://pith.science/pith/MCYNG54FMM74BLASM5OMVQKGDV","bundle":"https://pith.science/pith/MCYNG54FMM74BLASM5OMVQKGDV/bundle.json","state":"https://pith.science/pith/MCYNG54FMM74BLASM5OMVQKGDV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MCYNG54FMM74BLASM5OMVQKGDV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:MCYNG54FMM74BLASM5OMVQKGDV","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":"58e98477621291f8569dc0c279bd252a0b496b574d21a26e9188f320d8d2a5b9","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-02-01T00:33:21Z","title_canon_sha256":"33a9a211515ced3e215194accef8df66483c182165a90f379bf8d027480135ce"},"schema_version":"1.0","source":{"id":"2402.00253","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.00253","created_at":"2026-05-18T04:33:37Z"},{"alias_kind":"arxiv_version","alias_value":"2402.00253v2","created_at":"2026-05-18T04:33:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.00253","created_at":"2026-05-18T04:33:37Z"},{"alias_kind":"pith_short_12","alias_value":"MCYNG54FMM74","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"MCYNG54FMM74BLAS","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"MCYNG54F","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:78f62bafac29962b71ece0688b17b18966b897b9d113a548378c6caca347f962","target":"graph","created_at":"2026-05-18T04:33:37Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Hallucination, defined as the misalignment between factual visual content and corresponding textual generation, poses a significant challenge of utilizing LVLMs, and this survey establishes an overview to facilitate future mitigation."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That hallucinations can be consistently defined and isolated across diverse LVLM architectures and tasks without significant overlap with other error types such as reasoning failures."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"This survey reviews the definition, symptoms, evaluation benchmarks, root causes, and mitigation methods for hallucinations in large vision-language models."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Large vision-language models generate text that conflicts with input images, and this survey defines the problem while reviewing its symptoms, benchmarks, causes, and fixes."}],"snapshot_sha256":"94927963cf9c0a8d68a781ebc484168fa21c4d54e9d5a0bf6136a10c04d26c83"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"bfa4b190e5b5a0a408b1642f593f179644c4f57adb77771a6c7811986ef8d003"},"paper":{"abstract_excerpt":"Recent development of Large Vision-Language Models (LVLMs) has attracted growing attention within the AI landscape for its practical implementation potential. However, ``hallucination'', or more specifically, the misalignment between factual visual content and corresponding textual generation, poses a significant challenge of utilizing LVLMs. In this comprehensive survey, we dissect LVLM-related hallucinations in an attempt to establish an overview and facilitate future mitigation. Our scrutiny starts with a clarification of the concept of hallucinations in LVLMs, presenting a variety of hallu","authors_text":"Dapeng Chen, Hanchao Liu, Ke Wang, Liping Hou, Rongjun Li, Wei Peng, Wenyuan Xue, Xiutian Zhao, Yifei Chen","cross_cats":["cs.CL","cs.LG"],"headline":"Large vision-language models generate text that conflicts with input images, and this survey defines the problem while reviewing its symptoms, benchmarks, causes, and fixes.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-02-01T00:33:21Z","title":"A Survey on Hallucination in Large Vision-Language Models"},"references":{"count":56,"internal_anchors":21,"resolved_work":56,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Flamingo: a visual language model for few-shot learning","work_id":"994f306f-a295-42db-934f-7f15ed59e399","year":2022},{"cited_arxiv_id":"2308.12966","doi":"","is_internal_anchor":true,"ref_index":2,"title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","work_id":"cbc2bb21-b6bb-46c0-80bf-107e195ffe10","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Position-enhanced visual instruction tuning for multimodal large language models","work_id":"70a11f00-50ba-409c-b9da-619abaed18ea","year":null},{"cited_arxiv_id":"2310.09478","doi":"","is_internal_anchor":true,"ref_index":4,"title":"MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning","work_id":"fb62cd1b-3991-40be-a987-3cfa5772b5b5","year":null},{"cited_arxiv_id":"2312.14238","doi":"","is_internal_anchor":true,"ref_index":5,"title":"InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks","work_id":"d9e035c7-9e23-4cc2-ad3e-be080fbbf2d9","year":null}],"snapshot_sha256":"7ba873e9d79fbe63fdd9e6941ac8119a72ac6b9a9e74ca44ed6137c5a6884015"},"source":{"id":"2402.00253","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-13T22:05:38.517168Z","id":"bdbc8482-dc4c-481d-a809-04ade0131e21","model_set":{"reader":"grok-4.3"},"one_line_summary":"This survey reviews the definition, symptoms, evaluation benchmarks, root causes, and mitigation methods for hallucinations in large vision-language models.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Large vision-language models generate text that conflicts with input images, and this survey defines the problem while reviewing its symptoms, benchmarks, causes, and fixes.","strongest_claim":"Hallucination, defined as the misalignment between factual visual content and corresponding textual generation, poses a significant challenge of utilizing LVLMs, and this survey establishes an overview to facilitate future mitigation.","weakest_assumption":"That hallucinations can be consistently defined and isolated across diverse LVLM architectures and tasks without significant overlap with other error types such as reasoning failures."}},"verdict_id":"bdbc8482-dc4c-481d-a809-04ade0131e21"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0de41fec6929156439689bc3471b6e16cc7b856a9754c5a94d156de5a696080c","target":"record","created_at":"2026-05-18T04:33:37Z","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":"58e98477621291f8569dc0c279bd252a0b496b574d21a26e9188f320d8d2a5b9","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-02-01T00:33:21Z","title_canon_sha256":"33a9a211515ced3e215194accef8df66483c182165a90f379bf8d027480135ce"},"schema_version":"1.0","source":{"id":"2402.00253","kind":"arxiv","version":2}},"canonical_sha256":"60b0d37785633fc0ac12675ccac1461d5c6257f36b2a5a10468ee6d6560931d5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"60b0d37785633fc0ac12675ccac1461d5c6257f36b2a5a10468ee6d6560931d5","first_computed_at":"2026-05-18T04:33:37.170404Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:33:37.170404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pwJmCzGfSskX+ZdElXj8nnHLsPO3sNJ5z3nBys0FjZ1BDUOfY4RRYPalzt4vZoT5st621ASAjDvsBJ71iXRZAA==","signature_status":"signed_v1","signed_at":"2026-05-18T04:33:37.171109Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.00253","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0de41fec6929156439689bc3471b6e16cc7b856a9754c5a94d156de5a696080c","sha256:78f62bafac29962b71ece0688b17b18966b897b9d113a548378c6caca347f962"],"state_sha256":"3b80cd099e2a748e44b21a38835311cb36acddec1d729e251e6d06d8f76f4aad"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DctbcobLA+wrb4IpdZ78Lr99spUmR223XhE/SIM23NEalnkW04eeeGONxLbaCoVmtimEPYQFvax8+iVpkZ5lBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T02:35:53.106773Z","bundle_sha256":"bdf72abf4a1371c77e38d322297fecb2d44926c1aa01770884e7dd2faaf3aabf"}}