Summary of Take It Easy: Label-adaptive Self-rationalization For Fact Verification and Explanation Generation, by Jing Yang and Anderson Rocha
Take It Easy: Label-Adaptive Self-Rationalization for Fact Verification and Explanation Generation
by Jing Yang, Anderson Rocha
First submitted to arxiv on: 5 Oct 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 This paper proposes a novel approach to automated fact-checking and explanation generation for journalists. The existing methods rely heavily on three-class datasets, which are inadequate for capturing the complexity of misinformation in real-world scenarios. The proposed label-adaptive learning method extends self-rationalization techniques from natural language inference tasks to fact verification. The approach involves fine-tuning a model to learn veracity prediction and then adapting it to learn self-rationalization using annotated explanations. Experimental results demonstrate significant improvements in veracity prediction (over 10% increase in Macro F1) on both PubHealth and AVeriTec datasets, outperforming the GPT-4 model. Furthermore, the paper explores the potential of low-budget learning with synthetic data by generating explanations from large language models like GPT-4-turbo, GPT-3.5-turbo, and Llama-3-8B. The proposed label-adaptive self-rationalization approach presents a promising direction for future research on real-world explainable fact-checking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps journalists fight misinformation by developing new ways to check if statements are true or false. Current methods have limitations, as they rely on simple “true”, “false”, or “undecided” labels, which don’t accurately reflect the complexity of misinformation in real life. The proposed approach uses a special type of learning called self-rationalization, which helps explain why a statement is true or false. Experimental results show that this new method performs better than previous ones on two datasets. Additionally, the paper explores how to create explanations using artificial intelligence language models at a lower cost. This research has the potential to improve fact-checking and make it more reliable. |
Keywords
» Artificial intelligence » Fine tuning » Gpt » Inference » Llama » Synthetic data
