| 标题 |
 IMFND: In-context multimodal fake news detection with large visual-language models IMFND：基于大型视觉语言模型的上下文多模态假新闻检测
相关领域
计算机科学
背景（考古学）
视觉语言
自然语言处理
人工智能
语言学
历史
哲学
考古
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| 网址 | |
| DOI | |
| 其它 | Despite recent advancements, studies indicate that large language models (LLMs), such as GPT-3.5-turbo, often perform worse than well-trained small models, such as BERT, in fake news detection (FND). Similarly, while large visual-language models (LVLMs) demonstrate exceptional capabilities in visual-language reasoning across diverse cross-modal benchmarks, their performance in FND remains unexplored. This paper examines the FND capabilities of LVLMs in comparison to a small but adeptly trained contrastive language-image pre-training (CLIP) model in a zero-shot setting. The results reveal that LVLMs achieve performance comparable to small models. Building on this, the study further explores the integration of standard in-context learning (ICL) with LVLMs, observing modest yet inconsistent improvements in FND performance. To address these limitations, this paper introduces the In-context Multimodal Fake News Detection (IMFND) framework, |
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(2025-6-4)
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