English

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

Computation and Language 2024-08-02 v2

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.

Keywords

Cite

@article{arxiv.2407.21441,
  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}
}

Comments

Accepted in CIKM 2024 as a short paper 4 pages and 1 page references. Fixed typo in author name

R2 v1 2026-06-28T17:59:05.411Z