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        <title>Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models</title>
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    <abstract>In this paper, we investigate the phenomena of “selection biases” in Large Language Models (LLMs), focusing on problems where models are tasked with choosing the optimal option from an ordered sequence. We delve into biases related to option order and token usage, which significantly impact LLMs’ decision-making processes. We also quantify the impact of these biases through an extensive empirical analysis across multiple models and tasks. Furthermore, we propose mitigation strategies to enhance model performance. Our key contributions are threefold: 1) Precisely quantifying the influence of option order and token on LLMs, 2) Developing strategies to mitigate the impact of token and order sensitivity to enhance robustness, and 3) Offering a detailed analysis of sensitivity across models and tasks, which informs the creation of more stable and reliable LLM applications for selection problems.</abstract>
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%0 Conference Proceedings
%T Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models
%A Wei, Sheng-Lun
%A Wu, Cheng-Kuang
%A Huang, Hen-Hsen
%A Chen, Hsin-Hsi
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wei-etal-2024-unveiling
%X In this paper, we investigate the phenomena of “selection biases” in Large Language Models (LLMs), focusing on problems where models are tasked with choosing the optimal option from an ordered sequence. We delve into biases related to option order and token usage, which significantly impact LLMs’ decision-making processes. We also quantify the impact of these biases through an extensive empirical analysis across multiple models and tasks. Furthermore, we propose mitigation strategies to enhance model performance. Our key contributions are threefold: 1) Precisely quantifying the influence of option order and token on LLMs, 2) Developing strategies to mitigate the impact of token and order sensitivity to enhance robustness, and 3) Offering a detailed analysis of sensitivity across models and tasks, which informs the creation of more stable and reliable LLM applications for selection problems.
%R 10.18653/v1/2024.findings-acl.333
%U https://aclanthology.org/2024.findings-acl.333/
%U https://doi.org/10.18653/v1/2024.findings-acl.333
%P 5598-5621

Markdown (Informal)

[Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models](https://aclanthology.org/2024.findings-acl.333/) (Wei et al., Findings 2024)

• Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models (Wei et al., Findings 2024)

ACL

• Sheng-Lun Wei, Cheng-Kuang Wu, Hen-Hsen Huang, and Hsin-Hsi Chen. 2024. Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2024, pages 5598–5621, Bangkok, Thailand. Association for Computational Linguistics.