  Recently, large language model (LLM)-based preference evaluation has been
widely adopted to compare pairs of model responses. However, a severe bias
towards lengthy responses has been observed, raising concerns about the
reliability of this evaluation method. In this work, we designed a series of
controlled experiments to study the major impacting factors of the metric of
LLM-based preference evaluation, i.e., win rate, and conclude that the win rate
is affected by two axes of model response: desirability and information mass,
where the former is length-independent and related to trustworthiness, and the
latter is length-dependent and can be represented by conditional entropy. We
find that length impacts the existing evaluations by influencing information
mass. However, a reliable evaluation metric should not only assess content
quality but also ensure that the assessment is not confounded by extraneous
factors such as response length. Therefore, we propose a simple yet effective
adjustment, AdapAlpaca, to the existing practice of win rate measurement.
Specifically, by adjusting the lengths of reference answers to match the test
model's answers within the same interval, we debias information mass relative
to length, ensuring a fair model evaluation.
