Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T14:55:14.528553Z
Paper Citation Record · LEDGER
As of 11 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2501.14728.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T14:55:14.528553Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
40 of 40 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 4c042525-e697-4bf3-a688-7f498be4a594 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Fact-Saboteurs: A taxonomy of evidence manipula- tion attacks against fact-verification systems
Reference 1
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Observation 145677a3-5d2c-4157-a8fc-f3f73a27aa03 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection FACTIFY3M: A benchmark for multimodal fact verification with explain- ability through 5W question-answering
Reference 7
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Observation 5b4caed2-85f7-45a8-954c-e5fdf6677e7e · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Can llm- generated misinformation be detected? In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024,
Reference 8
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Observation 91776f8b-6336-4e52-a749-294de6f49606 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Synthetic disinformation attacks on au- tomated fact verification systems
Reference 10
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Observation 9dc43579-502b-411f-844b-8f38cbd57c90 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection A survey on automated fact-checking
Reference 11
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Observation 41f7158e-2e86-49aa-9437-2e9aa9a96c8b · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Multimedia se- mantic integrity assessment using joint embedding of im- ages and text
Reference 12
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Observation c78cc329-e15b-4b8a-a19f-645a4ddc8530 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Aligning large language models through synthetic feedback
Reference 14
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Observation e5ba7479-3a4b-4f7c-ab57-a4921a64ad6d · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection VisualBERT: A Simple and Performant Baseline for Vision and Language
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Observation cf591eff-711f-470c-b647-c0ff99432c40 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media
Reference 16
Source-reported events for the cited work
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Observation 8e5a1068-fe2a-4eac-8124-855fef80e046 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Multimodal analytics for real-world news using measures of cross-modal entity consistency
Reference 17
Source-reported events for the cited work
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Observation d65b309f-ce05-415c-a6a0-2ec47a2e4408 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection GPT-4 Technical Report
Reference 18
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Observation 72d48298-1363-4d29-8df9-35d378062f1c · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Hello GPT-4o,
Reference 19
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Observation d084ef61-37a0-4bab-9707-f5ef91e0ae53 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection [Pan et al., 2023a] Liangming Pan, Wenhu Chen, Min-Yen Kan, and William Yang Wang
Reference 20
Source-reported events for the cited work
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Observation f0be1cd7-4868-49ed-a873-11b1eac192cb · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection On the risk of misinformation pollution with large lan- guage models
Reference 21
Source-reported events for the cited work
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Observation 29cda262-fa81-441f-baea-bd56e9512eb9 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection RED-DOT: Multimodal Fact-checking via Relevant Evidence Detection
Reference 22
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Observation 5fb0ee58-a529-4c6e-bba4-f730fe9b7c8d · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection VERITE: a robust benchmark for multimodal misinformation detection accounting for unimodal bias
Reference 23
Source-reported events for the cited work
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Observation 81d3b382-effe-460c-8895-089d6e10866d · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection SNIFFER: Multimodal large language model for explainable out-of-context misinformation de- tection
Reference 24
Source-reported events for the cited work
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Observation 77124d3a-f020-4a79-a86c-4341ac25c479 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Learning transferable visual models from nat- ural language supervision
Reference 25
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Observation edd1df4b-8c6e-4127-b66f-f9be3bbe2056 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Hierarchical Text-Conditional Image Generation with CLIP Latents
Reference 26
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Observation e2add05c-6b31-4b78-93f4-65f4e3edb08b · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection High-resolution image synthesis with latent diffusion models
Reference 27
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Observation 02a6aa43-8d99-489a-ae5f-f1594a3ee846 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Countering misinformation via emotional response generation
Reference 28
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Observation ea6f7416-4559-4d9e-a419-d73d504b3f7c · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Deep multimodal image- repurposing detection
Reference 29
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Observation 1087a163-18e1-4695-a436-1b5ae60fd9c8 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Position: Will we run out of data? lim- its of LLM scaling based on human-generated data
Reference 32
Source-reported events for the cited work
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Observation d17b12e7-e14a-4f15-9059-52c3bb6bd39c · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
Reference 33
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Observation 2a2398c0-3f4c-4303-88ee-e936c6d99271 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Fake news in sheep’s clothing: Robust fake news detection against llm-empowered style attacks
Reference 34
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Observation f797db4a-0547-4a92-b78b-9322b1b06c0c · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Wagner, Danqi Chen, and Prateek Mittal
Reference 35
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Observation dfae8f99-7de8-4746-ae19-be743ea6e4ac · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection End-to-end multimodal fact-checking and explanation generation: A challenging dataset and models
Reference 36
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Observation 1df87265-7a19-49e6-aabe-6615ebcadcb1 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Don’t take this out of con- text!: On the need for contextual models and evaluations for stylistic rewriting
Reference 37
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Observation 56508c07-aa32-4881-ac84-3a573d83bb95 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Support or refute: Analyzing the stance of evidence to detect out-of-context mis- and disin- formation
Reference 38
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Observation e2372caa-6f4a-44ce-99df-52e08a47c394 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Instruction tuning for large language models: A survey
Reference 39
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Observation abed5fce-793c-4a2b-81f8-495374d4546c · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Interpretable Detection of Out-of-Context Misinformation with Neural-Symbolic-Enhanced Large Multimodal Model
Reference 40
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Observation 339f5757-5da8-4107-9a82-8bd5f1172f60 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Evidence-based factual error correction
Reference 2015
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Observation 90163b0e-2176-4407-8686-01f2575cb874 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Survey of hallucina- tion in natural language generation
Reference 2017
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Observation cd22ee01-8c4d-47df-89ed-940c53d6a094 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Very deep convolutional networks for large-scale image recognition
Reference 2018
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Observation 6cad3fb0-2a4f-432c-a2af-b8372dfcfa6f · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Hallucinated but factual! inspecting the factuality of hallu- cinations in abstractive summarization
Reference 2019
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 86adb648-2876-49db-961f-6d2d2d89877d · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Data scarcity, robustness and extreme multi- label classification
Reference 2020
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Observation 95581d40-2c8d-489e-b580-5861b3537649 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Generating label cohesive and well-formed adversarial claims
Reference 2021
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Observation a73e95b7-0eab-4221-b026-94a463bb5f98 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning
Reference 2022
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Observation df05720a-f639-4343-9287-2f1d3cbde40c · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Open-domain, content-based, multi- modal fact-checking of out-of-context images via online resources
Reference 2023
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Observation 84106558-a594-4ff4-a210-6eeec63a45a1 · outbound
Mitigating GenAI-powered Evidence Pollution for Out-of-Context Multimodal Misinformation Detection Unresolved cited work
Reference 2024
Source-reported events for the cited work
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No inbound Pith citation observations are available.