Summary of Surprising Efficacy Of Fine-tuned Transformers For Fact-checking Over Larger Language Models, by Vinay Setty
Surprising Efficacy of Fine-Tuned Transformers for Fact-Checking over Larger Language Models
by Vinay Setty
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents research on building an end-to-end fact-checking pipeline that can handle over 90 languages. The authors demonstrate the effectiveness of fine-tuning Transformer models for specific fact-checking tasks, such as claim detection and veracity prediction, outperforming large language models like GPT-4, GPT-3.5-Turbo, and Mistral-7b. However, they also show that LLMs excel in generative tasks like question decomposition for evidence retrieval. The study evaluates the performance of fine-tuned models in a multilingual setting and complex claims that include numerical quantities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about building a way to check if what people say is true or not, using computers. They tried different ways to do this and found that making special machines for fact-checking works better than just using big language models. But the big language models are good at coming up with new questions to help find evidence. The study shows that this fact-checking pipeline can work well in many languages and even with complicated claims. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Transformer
