QuestGen: Effectiveness of Question Generation Methods for Fact-Checking Applications
<|reference_start|>QuestGen: Effectiveness of Question Generation Methods for Fact-Checking Applications: Verifying fact-checking claims poses a significant challenge, even for humans. Recent approaches have demonstrated that decomposing claims into relevant questions to gather evidence enhances the efficiency of the fact-checking process. In this paper, we provide empirical evidence showing that this question decomposition can be effectively automated. We demonstrate that smaller generative models, fine-tuned for the question generation task using data augmentation from various datasets, outperform large language models by up to 8%. Surprisingly, in some cases, the evidence retrieved using machine-generated questions proves to be significantly more effective for fact-checking than that obtained from human-written questions. We also perform manual evaluation of the decomposed questions to assess the quality of the questions generated.<|reference_end|>
@article{setty2024questgen:,
    title={QuestGen: Effectiveness of Question Generation Methods for Fact-Checking
  Applications},
    author={Ritvik Setty and Vinay Setty},
    journal={arXiv preprint arXiv:2407.21441},
    year={2024},
    doi={10.1145/3627673.3679985},
    archivePrefix={arXiv},
    eprint={2407.21441},
    primaryClass={cs.CL}
}