@inproceedings{hu-etal-2025-explaining,
title = "Explaining Length Bias in {LLM}-Based Preference Evaluations",
author = "Hu, Zhengyu and
Song, Linxin and
Zhang, Jieyu and
Xiao, Zheyuan and
Wang, Tianfu and
Chen, Zhengyu and
Yuan, Nicholas Jing and
Lian, Jianxun and
Ding, Kaize and
Xiong, Hui",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.358/",
pages = "6763--6794",
ISBN = "979-8-89176-335-7",
abstract = "The use of large language models (LLMs) as judges, particularly in preference comparisons, has become widespread, but this reveals a notable bias towards longer responses, undermining the reliability of such evaluations. To better understand such bias, we propose to decompose the preference evaluation metric, specifically the win rate, into two key components: desirability and information mass, where the former is length-independent and related to trustworthiness such as correctness, toxicity, and consistency, and the latter is length-dependent and represents the amount of information in the response. We empirically demonstrated the decomposition through controlled experiments and found that response length impacts evaluations by influencing information mass. To derive a reliable evaluation metric that assesses content quality without being confounded by response length, we propose AdapAlpaca, a simple yet effective adjustment to win rate measurement. Specifically, AdapAlpaca ensures a fair comparison of response quality by aligning the lengths of reference and test model responses under equivalent length intervals."
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<abstract>The use of large language models (LLMs) as judges, particularly in preference comparisons, has become widespread, but this reveals a notable bias towards longer responses, undermining the reliability of such evaluations. To better understand such bias, we propose to decompose the preference evaluation metric, specifically the win rate, into two key components: desirability and information mass, where the former is length-independent and related to trustworthiness such as correctness, toxicity, and consistency, and the latter is length-dependent and represents the amount of information in the response. We empirically demonstrated the decomposition through controlled experiments and found that response length impacts evaluations by influencing information mass. To derive a reliable evaluation metric that assesses content quality without being confounded by response length, we propose AdapAlpaca, a simple yet effective adjustment to win rate measurement. Specifically, AdapAlpaca ensures a fair comparison of response quality by aligning the lengths of reference and test model responses under equivalent length intervals.</abstract>
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%0 Conference Proceedings
%T Explaining Length Bias in LLM-Based Preference Evaluations
%A Hu, Zhengyu
%A Song, Linxin
%A Zhang, Jieyu
%A Xiao, Zheyuan
%A Wang, Tianfu
%A Chen, Zhengyu
%A Yuan, Nicholas Jing
%A Lian, Jianxun
%A Ding, Kaize
%A Xiong, Hui
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F hu-etal-2025-explaining
%X The use of large language models (LLMs) as judges, particularly in preference comparisons, has become widespread, but this reveals a notable bias towards longer responses, undermining the reliability of such evaluations. To better understand such bias, we propose to decompose the preference evaluation metric, specifically the win rate, into two key components: desirability and information mass, where the former is length-independent and related to trustworthiness such as correctness, toxicity, and consistency, and the latter is length-dependent and represents the amount of information in the response. We empirically demonstrated the decomposition through controlled experiments and found that response length impacts evaluations by influencing information mass. To derive a reliable evaluation metric that assesses content quality without being confounded by response length, we propose AdapAlpaca, a simple yet effective adjustment to win rate measurement. Specifically, AdapAlpaca ensures a fair comparison of response quality by aligning the lengths of reference and test model responses under equivalent length intervals.
%U https://aclanthology.org/2025.findings-emnlp.358/
%P 6763-6794
Markdown (Informal)
[Explaining Length Bias in LLM-Based Preference Evaluations](https://aclanthology.org/2025.findings-emnlp.358/) (Hu et al., Findings 2025)
ACL
- Zhengyu Hu, Linxin Song, Jieyu Zhang, Zheyuan Xiao, Tianfu Wang, Zhengyu Chen, Nicholas Jing Yuan, Jianxun Lian, Kaize Ding, and Hui Xiong. 2025. Explaining Length Bias in LLM-Based Preference Evaluations. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 6763–6794, Suzhou, China. Association for Computational Linguistics.