@inproceedings{mohammadkhani-etal-2024-zero,
title = "Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking",
author = "Mohammadkhani, Mohammad Ghiasvand and
Mohammadkhani, Ali Ghiasvand and
Beigy, Hamid",
editor = "Schlichtkrull, Michael and
Chen, Yulong and
Whitehouse, Chenxi and
Deng, Zhenyun and
Akhtar, Mubashara and
Aly, Rami and
Guo, Zhijiang and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Mittal, Arpit and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.fever-1.9",
pages = "86--90",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking
%A Mohammadkhani, Mohammad Ghiasvand
%A Mohammadkhani, Ali Ghiasvand
%A Beigy, Hamid
%Y Schlichtkrull, Michael
%Y Chen, Yulong
%Y Whitehouse, Chenxi
%Y Deng, Zhenyun
%Y Akhtar, Mubashara
%Y Aly, Rami
%Y Guo, Zhijiang
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Mittal, Arpit
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F mohammadkhani-etal-2024-zero
%X 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.
%U https://aclanthology.org/2024.fever-1.9
%P 86-90
Markdown (Informal)
[Zero-Shot Learning and Key Points Are All You Need for Automated Fact-Checking](https://aclanthology.org/2024.fever-1.9) (Mohammadkhani et al., FEVER 2024)
ACL