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

arXiv:2403.10281v1 Announce Type: cross
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 …

abstract arxiv challenges classification components context cs.ai cs.cl cs.lg diverse explore fine-tuning framework in-context learning language language models large language large language models llms paper question question answering standard team text text classification type verification