Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking
This work addresses the challenge of preventing the spread of false information online, but it is incremental as it builds on existing LLM capabilities with a simple framework.
The authors tackled automated fact-checking by introducing a straightforward framework based on Zero-Shot Learning and Key Points (ZSL-KeP), which robustly improved the baseline and achieved 10th place on the AVeriTeC shared task dataset.
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.