Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917614667694080 |
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| author | Chiang, Shang-Hsuan Lo, Ming-Chih Chao, Lin-Wei Peng, Wen-Chih |
| author_facet | Chiang, Shang-Hsuan Lo, Ming-Chih Chao, Lin-Wei Peng, Wen-Chih |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10281 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning Chiang, Shang-Hsuan Lo, Ming-Chih Chao, Lin-Wei Peng, Wen-Chih Computation and Language Artificial Intelligence Machine Learning 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. |
| title | Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2403.10281 |