@inproceedings{zhao-etal-2024-pacar-automated,
title = "{PACAR}: Automated Fact-Checking with Planning and Customized Action Reasoning Using Large Language Models",
author = "Zhao, Xiaoyan and
Wang, Lingzhi and
Wang, Zhanghao and
Cheng, Hong and
Zhang, Rui and
Wong, Kam-Fai",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1099",
pages = "12564--12573",
abstract = "In an era characterized by the rapid proliferation of information, the pervasive issues of misinformation and disinformation have significantly impacted numerous individuals. Consequently, the evaluation of information{'}s truthfulness and accuracy has garnered substantial attention among researchers. In this work, we present a novel fact-checking framework called PACAR, fact-checking based on planning and customized action reasoning using LLMs. It comprises four modules: a claim decomposer with self-reflection, an LLM-centric planner module, an executor for carrying out planned actions, and a verifier module that assesses veracity and generates explanations based on the overall reasoning process. Unlike previous work that employs single-path decision-making and single-step verdict prediction, PACAR focuses on the use of LLMs in dynamic planning and execution of actions. Furthermore, in contrast to previous work that relied primarily on general reasoning, we introduce tailored actions such as numerical reasoning and entity disambiguation to effectively address potential challenges in fact-checking. Our PACAR framework, incorporating LLM-centric planning along with customized action reasoning, significantly outperforms baseline methods across three datasets from different domains and with varying complexity levels. Additional experiments, including multidimensional and sliced observations, demonstrate the effectiveness of PACAR and offer valuable insights for the advancement of automated fact-checking.",
}
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<abstract>In an era characterized by the rapid proliferation of information, the pervasive issues of misinformation and disinformation have significantly impacted numerous individuals. Consequently, the evaluation of information’s truthfulness and accuracy has garnered substantial attention among researchers. In this work, we present a novel fact-checking framework called PACAR, fact-checking based on planning and customized action reasoning using LLMs. It comprises four modules: a claim decomposer with self-reflection, an LLM-centric planner module, an executor for carrying out planned actions, and a verifier module that assesses veracity and generates explanations based on the overall reasoning process. Unlike previous work that employs single-path decision-making and single-step verdict prediction, PACAR focuses on the use of LLMs in dynamic planning and execution of actions. Furthermore, in contrast to previous work that relied primarily on general reasoning, we introduce tailored actions such as numerical reasoning and entity disambiguation to effectively address potential challenges in fact-checking. Our PACAR framework, incorporating LLM-centric planning along with customized action reasoning, significantly outperforms baseline methods across three datasets from different domains and with varying complexity levels. Additional experiments, including multidimensional and sliced observations, demonstrate the effectiveness of PACAR and offer valuable insights for the advancement of automated fact-checking.</abstract>
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%0 Conference Proceedings
%T PACAR: Automated Fact-Checking with Planning and Customized Action Reasoning Using Large Language Models
%A Zhao, Xiaoyan
%A Wang, Lingzhi
%A Wang, Zhanghao
%A Cheng, Hong
%A Zhang, Rui
%A Wong, Kam-Fai
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F zhao-etal-2024-pacar-automated
%X In an era characterized by the rapid proliferation of information, the pervasive issues of misinformation and disinformation have significantly impacted numerous individuals. Consequently, the evaluation of information’s truthfulness and accuracy has garnered substantial attention among researchers. In this work, we present a novel fact-checking framework called PACAR, fact-checking based on planning and customized action reasoning using LLMs. It comprises four modules: a claim decomposer with self-reflection, an LLM-centric planner module, an executor for carrying out planned actions, and a verifier module that assesses veracity and generates explanations based on the overall reasoning process. Unlike previous work that employs single-path decision-making and single-step verdict prediction, PACAR focuses on the use of LLMs in dynamic planning and execution of actions. Furthermore, in contrast to previous work that relied primarily on general reasoning, we introduce tailored actions such as numerical reasoning and entity disambiguation to effectively address potential challenges in fact-checking. Our PACAR framework, incorporating LLM-centric planning along with customized action reasoning, significantly outperforms baseline methods across three datasets from different domains and with varying complexity levels. Additional experiments, including multidimensional and sliced observations, demonstrate the effectiveness of PACAR and offer valuable insights for the advancement of automated fact-checking.
%U https://aclanthology.org/2024.lrec-main.1099
%P 12564-12573
Markdown (Informal)
[PACAR: Automated Fact-Checking with Planning and Customized Action Reasoning Using Large Language Models](https://aclanthology.org/2024.lrec-main.1099) (Zhao et al., LREC-COLING 2024)
ACL