Summary of Team Trifecta at Factify5wqa: Setting the Standard in Fact Verification with Fine-tuning, by Shang-hsuan Chiang et al.
Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning
by Shang-Hsuan Chiang, Ming-Chih Lo, Lin-Wei Chao, Wen-Chih Peng
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Pre-CoFactv3 framework for fact verification combines Question Answering and Text Classification components, utilizing In-Context Learning, Fine-tuned Large Language Models (LLMs), and the FakeNet model. The paper explores diverse approaches, comparing different Pre-trained LLMs, introducing FakeNet, and implementing various ensemble methods. Notably, the authors’ team 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to check if information is true or false has been developed! The Pre-CoFactv3 framework uses special computer models to answer questions and classify text as true or false. This helps solve the problem of fake news and misinformation online. The researchers tested different approaches to see what worked best, and they were very successful in a competition called Factify 3.0. |
Keywords
* Artificial intelligence * Question answering * Text classification
