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

TL;DR

Pre-CoFactv3, a comprehensive framework comprised of Question Answering and Text Classification components for fact verification, is presented, leveraging In-Context Learning, Fine-tuned Large Language Models, and the FakeNet model to address the challenges of fact verification.

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.

Figures (3)

• Figure 1: The overview of our Pre-CoFactv3 framework
• Figure 2: The overview of FakeNet
• Figure 3: The confusion matrix for these three models and the ensemble model