LRQ-Fact: LLM-Generated Relevant Questions for Multimodal Fact-Checking

• URL: http://arxiv.org/abs/2410.04616v1
• Date: Sun, 6 Oct 2024 20:33:22 GMT
• Title: LRQ-Fact: LLM-Generated Relevant Questions for Multimodal Fact-Checking
• Authors: Alimohammad Beigi, Bohan Jiang, Dawei Li, Tharindu Kumarage, Zhen Tan, Pouya Shaeri, Huan Liu,
• Abstract summary: We propose a fully-automated framework, LRQ-Fact, for multimodal fact-checking. It generates comprehensive questions and answers for probing multimodal content. It then evaluates both the original content and the generated questions and answers to assess the overall veracity.
• Score: 14.647261841209767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human fact-checkers have specialized domain knowledge that allows them to formulate precise questions to verify information accuracy. However, this expert-driven approach is labor-intensive and is not scalable, especially when dealing with complex multimodal misinformation. In this paper, we propose a fully-automated framework, LRQ-Fact, for multimodal fact-checking. Firstly, the framework leverages Vision-Language Models (VLMs) and Large Language Models (LLMs) to generate comprehensive questions and answers for probing multimodal content. Next, a rule-based decision-maker module evaluates both the original content and the generated questions and answers to assess the overall veracity. Extensive experiments on two benchmarks show that LRQ-Fact improves detection accuracy for multimodal misinformation. Moreover, we evaluate its generalizability across different model backbones, offering valuable insights for further refinement.

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