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arXiv · 2407.21441

QuestGen: Effectiveness of Question Generation Methods for Fact-Checking Applications

Ritvik Setty·Vinay Setty
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Abstract

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.

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Ritvik Setty, Vinay Setty. 2024-07-31. QuestGen: Effectiveness of Question Generation Methods for Fact-Checking Applications. https://doi.org/10.1145/3627673.3679985

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