Entailed Opinion Matters: Improving the Fact-Checking Performance of Language Models by Relying on their Entailment Ability

TL;DR

This paper introduces a novel fact-checking method that leverages the entailment capabilities of generative language models to improve the accuracy of encoder-only models, addressing real-world deployment challenges.

Contribution

It proposes a new learning paradigm using evidence classification and entailment-based justifications from GLMs to enhance fact-checking performance of ELMs.

Findings

Improved fact-checking accuracy over prior methods

Effective use of entailment-based explanations

Robustness across different domains

Abstract

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.