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        <title>Can LLMs Verify Arabic Claims? Evaluating the Arabic Fact-Checking Abilities of Multilingual LLMs</title>
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    <name type="personal">
        <namePart type="given">Ayushman</namePart>
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        <namePart type="given">Aryan</namePart>
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    <name type="personal">
        <namePart type="given">Evan</namePart>
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            <title>Proceedings of the 1st Workshop on NLP for Languages Using Arabic Script</title>
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    <abstract>Large language models (LLMs) have demonstrated potential in fact-checking claims, yet their capabilities in verifying claims in multilingual contexts remain largely understudied. This paper investigates the efficacy of various prompting techniques, viz. Zero-Shot, English Chain-of-Thought, Self-Consistency, and Cross-Lingual Prompting, in enhancing the fact-checking and claim-verification abilities of LLMs for Arabic claims. We utilize 771 Arabic claims sourced from the X-fact dataset to benchmark the performance of four LLMs. To the best of our knowledge, ours is the first study to benchmark the inherent Arabic fact-checking abilities of LLMs stemming from their knowledge of Arabic facts, using a variety of prompting methods. Our results reveal significant variations in accuracy across different prompting methods. Our findings suggest that Cross-Lingual Prompting outperforms other methods, leading to notable performance gains.</abstract>
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%0 Conference Proceedings
%T Can LLMs Verify Arabic Claims? Evaluating the Arabic Fact-Checking Abilities of Multilingual LLMs
%A Gupta, Ayushman
%A Singhal, Aryan
%A Law, Thomas
%A Rao, Veekshith
%A Duan, Evan
%A Li, Ryan Luo
%Y El-Haj, Mo
%S Proceedings of the 1st Workshop on NLP for Languages Using Arabic Script
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F gupta-etal-2025-llms
%X Large language models (LLMs) have demonstrated potential in fact-checking claims, yet their capabilities in verifying claims in multilingual contexts remain largely understudied. This paper investigates the efficacy of various prompting techniques, viz. Zero-Shot, English Chain-of-Thought, Self-Consistency, and Cross-Lingual Prompting, in enhancing the fact-checking and claim-verification abilities of LLMs for Arabic claims. We utilize 771 Arabic claims sourced from the X-fact dataset to benchmark the performance of four LLMs. To the best of our knowledge, ours is the first study to benchmark the inherent Arabic fact-checking abilities of LLMs stemming from their knowledge of Arabic facts, using a variety of prompting methods. Our results reveal significant variations in accuracy across different prompting methods. Our findings suggest that Cross-Lingual Prompting outperforms other methods, leading to notable performance gains.
%U https://aclanthology.org/2025.abjadnlp-1.12/
%P 104-113

Markdown (Informal)

[Can LLMs Verify Arabic Claims? Evaluating the Arabic Fact-Checking Abilities of Multilingual LLMs](https://aclanthology.org/2025.abjadnlp-1.12/) (Gupta et al., AbjadNLP 2025)

• Can LLMs Verify Arabic Claims? Evaluating the Arabic Fact-Checking Abilities of Multilingual LLMs (Gupta et al., AbjadNLP 2025)

ACL

• Ayushman Gupta, Aryan Singhal, Thomas Law, Veekshith Rao, Evan Duan, and Ryan Luo Li. 2025. Can LLMs Verify Arabic Claims? Evaluating the Arabic Fact-Checking Abilities of Multilingual LLMs. In Proceedings of the 1st Workshop on NLP for Languages Using Arabic Script, pages 104–113, Abu Dhabi, UAE. Association for Computational Linguistics.