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arXiv · 2403.10281

Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning

Shang-Hsuan Chiang·Ming-Chih Lo·Lin-Wei Chao·Wen-Chih Peng
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Abstract

In this paper, we present Pre-CoFactv3, a comprehensive framework comprised of Question Answering and Text Classification components for fact verification. Leveraging In-Context Learning, Fine-tuned Large Language Models (LLMs), and the FakeNet model, we address the challenges of fact verification. Our experiments explore diverse approaches, comparing different Pre-trained LLMs, introducing FakeNet, and implementing various ensemble methods. Notably, our team, Trifecta, secured first place in the AAAI-24 Factify 3.0 Workshop, surpassing the baseline accuracy by 103% and maintaining a 70% lead over the second competitor. This success underscores the efficacy of our approach and its potential contributions to advancing fact verification research.

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Shang-Hsuan Chiang, Ming-Chih Lo, Lin-Wei Chao, Wen-Chih Peng. 2024-03-15. Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning. https://arxiv.org/abs/2403.10281

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