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

Shang-Hsuan Chiang, Ming-Chih Lo, Lin-Wei Chao, Wen-Chih Peng
3/15/2024
cs.CLcs.AIcs.LG

No official code found. Best implementation: AndyChiangSH/Pre-CoFactv3 (4 stars, last commit 2024-07-22).

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.

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Code Implementations(1)

AndyChiangSH/Pre-CoFactv3Community100%

Code for AAAI 2024 Workshop Paper: “Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning”

41Shell, PythonOct 28, 20232 years ago
ensemble-learningfact-verificationfine-tuningquestion-answeringtext-classification

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Cite this paper

@article{chiang2024team,
  title  = {Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning},
  author = {Shang-Hsuan Chiang and Ming-Chih Lo and Lin-Wei Chao and Wen-Chih Peng},
  year   = {2024},
  eprint = {2403.10281},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.10281v1}
}

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