Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning
<|reference_start|>Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning: 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.<|reference_end|>
@article{chiang2024team,
    title={Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification
  with Fine-Tuning},
    author={Shang-Hsuan Chiang, Ming-Chih Lo, Lin-Wei Chao and Wen-Chih Peng},
    journal={arXiv preprint arXiv:2403.10281},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10281},
    primaryClass={cs.CL cs.AI cs.LG}
}