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
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.