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.

翻译：本文提出Pre-CoFactv3综合框架，该框架集成问答与文本分类组件用于事实验证。通过利用情境学习（In-Context Learning）、微调大语言模型（LLMs）及FakeNet模型，我们应对事实验证中的挑战。实验探索了多种方法，比较不同预训练大语言模型，引入FakeNet架构，并实施多样化集成策略。值得关注的是，我们的Trifecta团队在AAAI-24 Factify 3.0研讨会中荣获第一名，以103%的准确率超越基准线，并以70%的领先优势保持第二名的相对差距。该成果充分验证了我们方法的有效性，及其对推动事实验证研究发展的潜在贡献。