Take It Easy: Label-Adaptive Self-Rationalization for Fact Verification and Explanation Generation

• URL: http://arxiv.org/abs/2410.04002v1
• Date: Sat, 5 Oct 2024 02:19:49 GMT
• Title: Take It Easy: Label-Adaptive Self-Rationalization for Fact Verification and Explanation Generation
• Authors: Jing Yang, Anderson Rocha,
• Abstract summary: Self-rationalization method is typically used in natural language inference tasks. We fine-tune a model to learn veracity prediction with annotated labels. We generate synthetic explanations from three large language models.
• Score: 15.94564349084642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computational methods to aid journalists in the task often require adapting a model to specific domains and generating explanations. However, most automated fact-checking methods rely on three-class datasets, which do not accurately reflect real-world misinformation. Moreover, fact-checking explanations are often generated based on text summarization of evidence, failing to address the relationship between the claim and the evidence. To address these issues, we extend the self-rationalization method--typically used in natural language inference (NLI) tasks--to fact verification. We propose a label-adaptive learning approach: first, we fine-tune a model to learn veracity prediction with annotated labels (step-1 model). Then, we fine-tune the step-1 model again to learn self-rationalization, using the same data and additional annotated explanations. Our results show that our label-adaptive approach improves veracity prediction by more than ten percentage points (Macro F1) on both the PubHealth and AVeriTec datasets, outperforming the GPT-4 model. Furthermore, to address the high cost of explanation annotation, we generated 64 synthetic explanations from three large language models: GPT-4-turbo, GPT-3.5-turbo, and Llama-3-8B and few-shot fine-tune our step-1 model. The few-shot synthetic explanation fine-tuned model performed comparably to the fully fine-tuned self-rationalization model, demonstrating the potential of low-budget learning with synthetic data. Our label-adaptive self-rationalization approach presents a promising direction for future research on real-world explainable fact-checking with different labeling schemes.

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