Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation
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Computer Science > Computation and Language
arXiv:2601.06600 (cs)
[Submitted on 10 Jan 2026 (v1), last revised 5 Jun 2026 (this version, v4)]
Title:Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation
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Abstract:Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues. While Multimodal Large Language Models (MLLMs) have demonstrated impressive reasoning capabilities, their robustness against misinformation entangled with cognitive biases remains under-explored. In this paper, we introduce a comprehensive evaluation framework using a high-quality, manually annotated dataset of 200 short videos spanning four health domains. This dataset provides fine-grained annotations for three deceptive patterns-experimental errors, logical fallacies, and fabricated claims-each verified by evidence such as national standards and academic literature. We evaluate eight frontier MLLMs across five modality settings. Experimental results demonstrate that Gemini-2.5-Pro achieves the highest performance in the multimodal setting with a belief score of 71.5/100, while o3 performs the worst at 35.2. Furthermore, we investigate social cues that induce false beliefs in videos and find that models are susceptible to biases like authoritative channel IDs.
| Comments: | Accepted to ACL 2026 (Findings) |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2601.06600 [cs.CL] |
| (or arXiv:2601.06600v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2601.06600
arXiv-issued DOI via DataCite
|
Submission history
From: Jen-Tse Huang [view email][v1] Sat, 10 Jan 2026 15:43:30 UTC (1,015 KB)
[v2] Thu, 30 Apr 2026 22:50:15 UTC (1,021 KB)
[v3] Fri, 15 May 2026 19:28:16 UTC (1,021 KB)
[v4] Fri, 5 Jun 2026 03:30:37 UTC (1,017 KB)
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