Explaining Length Bias in LLM -Based Preference Evaluations (2025)

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.

## introduction as llms are increasingly deployed across various domains of artificial intelligence, from natural language processing to complex decision-making systems @xcite @xcite @xcite @xcite , ensuring their performance, reliability, and fairness has become a critical challenge @xcite @xcite @xcite @xcite . llm-based auto-evaluators have emerged as a crucial tool in this context, offering a costeffective and scalable alternative to labor-intensive human evaluations @xcite @xcite @xcite . despite their advantages, these automated systems are not without their shortcomings, particularly concerning the introduction and perpetuation of biases @xcite @xcite . one of the important biases observed in llm-based evaluations is the preference for longer textual responses. previous empirical studies have explored a strong correlation between the length of response and its perceived quality represented by win rate @xcite @xcite . however, it is not reasonable to simply attribute the preference to length since length is only the surface factor for the quality of a sentence. therefore, in this work, we investigate the following question: what are the major factors contributing to the win rate? to solve this problem, we propose a new framework that decomposes the quality of a response, as measured by its win rate in pairwise comparisons, into two distinct components: (1) desirability, which is independent of length and reflects the trustworthiness of the response, encompassing factors such as correctness, toxicity, and consistency; and (2) information mass, which is dependent on length and represents the amount of information in the response, measurable through conditional entropy. we validate our hypothesis by testing win rates in two different scenarios: (i) comparing normal responses with those differing in desirability (e.g., logical to be desired and biased not desired), and (ii) comparing normal responses with concise and detailed responses, which vary in information mass. our experiments demonstrate that responses with negative desirability significantly decrease the win rate, whereas information mass, when not negatively influenced by desirability, is positively correlated with the win rate, thus confirming the effectiveness of our metric. following this finding, we design a new prompt called "quality enhancement" to improve information mass with positive desirability. this prompt enables gpt-4 to achieve state-of-the-art results on alpacaeval, increasing the win rate from 50.00% to 70.16%. through our decomposition of the quality of a response, we observe that response length impacts evaluations primarily by influencing information mass. however, a reliable evaluation metric should assess content quality without being confounded by extraneous factors such as response length @xcite @xcite , we further propose adapalpaca, a benchmark designed to improve evaluation fairness. by ensuring that responses are compared at the same length intervals, adapalpaca effectively mitigates length bias, enabling accurate content quality assessments (see figure 1 ). with adapalpaca, we further analyze length bias in direct preference optimization (dpo) @xcite to examine the findings in prior work @xcite @xcite ) that dpo lengthens model responses. specifically, we test tülu2 @xcite and tülu2-dpo models at 7b, 13b, and 70b scales on alpacaeval and adapalpaca. our results indicate that dpo leads to higher human preference, but this gain is amplified by response length, with alpacaeval showing higher win rates gain than adapalpaca. our major findings and contributions are as follows: correlation between length and win rate. previous research reveals that sentence length will influence the evaluation of trustworthiness. specifically, when using a gpt-4 to represent human preference, it will prefer to choose a long sentence rather than a short sentence @xcite @xcite @xcite @xcite @xcite . such preference will introduce a length-correlated bias and help the model with long-generation sentences gain a high score on human preference evaluation. although these approaches show a high correlation to human preference, debiasing such as automated evaluation is highly valuable. @xcite proposes a length-controlled (lc) win rate by removing the length-correlated term in the win rate regression model. the new lc win rate shows an even performance between concise and verbose input and a higher correlation when compared with human preference. ## understanding the major factors of win rate to interpret the correlation between length and win rate, we propose a new framework based on quality, which includes desirability (length-independent, related to trustworthiness) and information mass (length-dependent, represented by conditional entropy). we validate our hypothesis through two scenarios: (1) testing the impact of different desirability on win rate with the same information mass, and (2) testing the influence of different information mass on win rate with the same desirability. ## adaptive alpacaeval here, we analyze the phenomenon observed in prior works @xcite @xcite , which highlights a positive correlation between response length and win rate. intuitively, longer responses tend to encompass more information. to rigorously quantify this relationship, we use conditional entropy as information mass in a response z given an instruction x. this analysis is conducted without constraints on response desirability, ensuring the correlation between length and information mass remains independent of desirability factors. as shown in figure 4 , our analysis demonstrates a clear trend: as the length of a response increases, the information mass also grows. by integrating this observation with the findings from section 3.2, we conclude that the primary mechanism through which length affects win rate is its contribution to the overall information mass. adaptive alpacaeval (adapalpac) is based on the premise that a reliable evaluation metric should not only assess the content quality but also ensure that the assessment is not confounded by extraneous factors such as the length of the response. central to this approach is the concept of information mass, which is inherently dependent on response length and can be quantified using conditional entropy. our primary aim is to mitigate scenarios where merely extending the length of a response artificially inflates its conditional entropy and, thus, its perceived quality by annotators. this approach involves dynamically adjusting the evaluation cri-deciding whether a tweet is offensive requires analysis of the tweet content, context, use of language, and potential for harm or insult to individuals or groups. which answer do you prefer ? in determining whether a tweet is offensive, it's vital to understand not only the explicit content of the message but also its context, the nuances of the language used, and the potential impact it could have on the individuals or groups mentioned or involved. determining whether a tweet or any other statement is offensive can be subjective and often depends on various factors including the context in which it was said, the audience, and cultural norms. ## conclusion in this paper, we identify and address the critical issue of length bias in llm-based preference evaluations, which undermines the reliability of win rate metrics. by decomposing win rate into desirability and information mass, we offer a nuanced understanding of response quality. our proposed framework, adapalpaca, effectively mitigates length bias by dynamically adjusting reference answer lengths to match test model responses, ensuring fairer evaluation metrics. additionally, our analysis of dpo demonstrates that its gains in human preference are influenced by response length, underscoring the importance of unbiased evaluation benchmarks. overall, adapalpaca provides a robust tool for advancing reliable and equitable model evaluation.