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        <title>An Empirical Analysis on Large Language Models in Debate Evaluation</title>
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        <namePart type="given">Xinyi</namePart>
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    <name type="personal">
        <namePart type="given">Hangfeng</namePart>
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            <title>Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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            <namePart type="given">Lun-Wei</namePart>
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    <abstract>In this study, we investigate the capabilities and inherent biases of advanced large language models (LLMs) such as GPT-3.5 and GPT-4 in the context of debate evaluation. We discover that LLM‘s performance exceeds humans and surpasses the performance of state-of-the-art methods fine-tuned on extensive datasets. We additionally explore and analyze biases present in LLMs, including positional bias, lexical bias, order bias, which may affect their evaluative judgments. Our findings reveal a consistent bias in both GPT-3.5 and GPT-4 towards the second candidate response presented, attributed to prompt design. We also uncover a lexical bias in both GPT-3.5 and GPT-4, especially when label sets carry connotations such as numerical or sequential, highlighting the critical need for careful label verbalizer selection in prompt design. Additionally, our analysis indicates a tendency of both models to favor the debate‘s concluding side as the winner, suggesting an end-of-discussion bias.</abstract>
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    <identifier type="doi">10.18653/v1/2024.acl-short.44</identifier>
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%0 Conference Proceedings
%T An Empirical Analysis on Large Language Models in Debate Evaluation
%A Liu, Xinyi
%A Liu, Pinxin
%A He, Hangfeng
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F liu-etal-2024-empirical
%X In this study, we investigate the capabilities and inherent biases of advanced large language models (LLMs) such as GPT-3.5 and GPT-4 in the context of debate evaluation. We discover that LLM‘s performance exceeds humans and surpasses the performance of state-of-the-art methods fine-tuned on extensive datasets. We additionally explore and analyze biases present in LLMs, including positional bias, lexical bias, order bias, which may affect their evaluative judgments. Our findings reveal a consistent bias in both GPT-3.5 and GPT-4 towards the second candidate response presented, attributed to prompt design. We also uncover a lexical bias in both GPT-3.5 and GPT-4, especially when label sets carry connotations such as numerical or sequential, highlighting the critical need for careful label verbalizer selection in prompt design. Additionally, our analysis indicates a tendency of both models to favor the debate‘s concluding side as the winner, suggesting an end-of-discussion bias.
%R 10.18653/v1/2024.acl-short.44
%U https://aclanthology.org/2024.luhme-short.44/
%U https://doi.org/10.18653/v1/2024.acl-short.44
%P 470-487

Markdown (Informal)

[An Empirical Analysis on Large Language Models in Debate Evaluation](https://aclanthology.org/2024.luhme-short.44/) (Liu et al., ACL 2024)

• An Empirical Analysis on Large Language Models in Debate Evaluation (Liu et al., ACL 2024)

ACL

• Xinyi Liu, Pinxin Liu, and Hangfeng He. 2024. An Empirical Analysis on Large Language Models in Debate Evaluation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 470–487, Bangkok, Thailand. Association for Computational Linguistics.