LRQ-Fact: LLM-Generated Relevant Questions for Multimodal Fact-Checking
<|reference_start|>LRQ-Fact: LLM-Generated Relevant Questions for Multimodal Fact-Checking: 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.<|reference_end|>
@article{beigi2024lrq-fact:,
    title={LRQ-Fact: LLM-Generated Relevant Questions for Multimodal Fact-Checking},
    author={Alimohammad Beigi, Bohan Jiang, Dawei Li, Tharindu Kumarage, Zhen Tan,
  Pouya Shaeri and Huan Liu},
    journal={arXiv preprint arXiv:2410.04616},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04616},
    primaryClass={cs.CL}
}