Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking

TL;DR

This paper presents ZSL-KeP, a simple yet effective zero-shot learning framework for automated fact-checking using large language models, achieving competitive results by leveraging key points and prompting strategies.

Contribution

The paper introduces a novel zero-shot learning framework called ZSL-KeP for automated fact-checking, demonstrating its effectiveness with minimal complexity.

Findings

Achieved 10th place on AVeriTeC dataset

Robustly improved baseline performance

Effective use of key points and prompting strategies

Abstract

Automated fact-checking is an important task because determining the accurate status of a proposed claim within the vast amount of information available online is a critical challenge. This challenge requires robust evaluation to prevent the spread of false information. Modern large language models (LLMs) have demonstrated high capability in performing a diverse range of Natural Language Processing (NLP) tasks. By utilizing proper prompting strategies, their versatility due to their understanding of large context sizes and zero-shot learning ability enables them to simulate human problem-solving intuition and move towards being an alternative to humans for solving problems. In this work, we introduce a straightforward framework based on Zero-Shot Learning and Key Points (ZSL-KeP) for automated fact-checking, which despite its simplicity, performed well on the AVeriTeC shared task dataset by robustly improving the baseline and achieving 10th place.