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.

本文介绍了 Pre-CoFactv3，一种包括问题回答和文本分类组件的综合框架，用于事实验证。通过利用上下文学习、经过微调的大型语言模型（LLMs）和 FakeNet 模型，我们解决了事实验证的挑战。我们的实验探索了不同的预训练 LLMs、引入 FakeNet 和实施各种集成方法。值得注意的是，我们的团队 Trifecta 在 AAAI-24 Factify 3.0 研讨会上获得了第一名，超过基准准确率 103%，并保持与第二名竞争者的 70% 领先优势。这一成功突显了我们方法的有效性以及对推进事实验证研究的潜在贡献。