Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917751014031360 |
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| author | Mohammadkhani, Mohammad Ghiasvand Mohammadkhani, Ali Ghiasvand Beigy, Hamid |
| author_facet | Mohammadkhani, Mohammad Ghiasvand Mohammadkhani, Ali Ghiasvand Beigy, Hamid |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08400 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking Mohammadkhani, Mohammad Ghiasvand Mohammadkhani, Ali Ghiasvand Beigy, Hamid Computation and Language 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. |
| title | Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2408.08400 |