蕴含观点重要性：通过依赖生成语言模型的蕴含能力提升事实核查性能
Entailed Opinion Matters: Improving the Fact-Checking Performance of Language Models by Relying on their Entailment Ability
AI总结：
本文提出通过生成语言模型的蕴含能力提升事实核查性能，通过证据分类和蕴含论证训练仅编码器的语言模型，以提高事实核查的准确性。
摘要：
自动事实核查一直是研究社区面临的挑战性任务。先前的工作探索了各种策略，如端到端训练、检索增强生成和提示工程，以构建稳健的事实核查系统。然而，其准确性尚不足以满足现实世界的部署需求。我们提出了一种新的学习范式，其中生成语言模型（GLMs）生成的证据分类和蕴含性论证被用来训练仅编码器的语言模型（ELMs）。我们进行了严谨的一系列实验，将我们的方法与最近的工作以及各种提示和微调策略进行比较。此外，我们还进行了消融研究、错误分析、模型解释质量分析和领域泛化研究，以提供对我们的方法的全面理解。
英文摘要：
Automated fact-checking has been a challenging task for the research community. Prior work has explored various strategies, such as end-to-end training, retrieval-augmented generation, and prompt engineering, to build robust fact-checking systems. However, their accuracy has not been high enough for real-world deployment. We, on the other hand, propose a new learning paradigm, where evidence classification and entailed justifications made by generative language models (GLMs) are used to train encoder-only language models (ELMs). We conducted a rigorous set of experiments, comparing our approach with recent works along with various prompting and fine-tuning strategies. Additionally, we performed ablation studies, error analysis, quality analysis of model explanations, and a domain generalisation study to provide a comprehensive understanding of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
