Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking (2024)

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 _**Z**ero-**S**hot **L**earning_ and _**Ke**y **P**oints_ (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.

## introduction the averitec task @xcite is designed to encourage the development of advanced frameworks for automated fact-checking, a critical task in nlp. with the rapid spread of information and misinformation online, automated factchecking is increasingly important. given the time-consuming nature of manual fact-checking, building an effective neural language model-based framework is valuable for saving time and costs, improving performance, and supporting human judgment. significant efforts are being made to automate this process within digital tools or llms @xcite . llms with billions of parameters offer extensive knowledge and strong reasoning capabilities that 1 code and data released at https://github.com/ mghiasvand1/zsl-kep can be customized for various tasks. designing effective and appropriate prompts is crucial in this customization process. recent utilization of llms can mainly be divided into two categories: finetuning and in-context learning (icl). given the enormous size of llms and the high computational cost associated with fine-tuning them, utilizing icl through zero-shot or few-shot prompting is much more efficient. explaining the reasoning behind a decision is crucial for user trust in automated fact-checking, as users need to understand the evidence behind the model's verdict @xcite . this work employs large language models (llms) with zero-shot learning (zsl), which offer advantages over simpler, classification-based models due to their long context windows and high reasoning capabilities. besides using powerful llms and effective prompting, accurate retrieval of relevant information is vital. this involves hierarchical, step-by-step prompting and decomposition-based retrieval methods @xcite . this paper describes the novel approach implemented by our team, ma-bros-h, for the averitec shared task, which integrates zsl and key point utilization within a unified and straightforward framework. ## related works to highlight a few recent research efforts in automated fact-checking, it is notable that @xcite ) provided explainability through summarization, and @xcite utilized the internal knowledge of pretrained language models such as bert @xcite within their framework. additionally, @xcite employed few-shot prompting for fact-checking, @xcite introduced a hierarchical, step-by-step prompting method that involves claim decomposition followed by step-by-step reasoning to predict the final verdict, and @xcite proposed a multi-agent debate strategy for explainable factchecking. ## methodology this section provides a detailed overview of the problem definition of averitec task, as well as the operational procedure of our zsl-kep model, as outlined in figure 1 . ## conclusion in this paper, we introduced zsl-kep, an effective yet straightforward framework for automated fact-checking. we utilized the zsl capability of llms and constructed key points for extensive retrieval to generate evidence in a question-andanswer pairs format, along with a final verdict. by relying solely on the icl capability of llms, our strategy operates without requiring any fine-tuning and is more straightforward compared to the baseline. our framework sets a new benchmark, indicating promising avenues for future research in related topics.