Beyond the Surface: Measuring Self-Preference in LLM Judgments
Recent studies show that large language models (LLMs) exhibit self-preference
bias when serving as judges, meaning they tend to favor their own responses
over those generated by other models. Existing methods typically measure this
bias by calculating the difference between the scores a judge model assigns to
its own responses and those it assigns to responses from other models. However,
this approach conflates self-preference bias with response quality, as
higher-quality responses from the judge model may also lead to positive score
differences, even in the absence of bias. To address this issue, we introduce
gold judgments as proxies for the actual quality of responses and propose the
DBG score, which measures self-preference bias as the difference between the
scores assigned by the judge model to its own responses and the corresponding
gold judgments. Since gold judgments reflect true response quality, the DBG
score mitigates the confounding effect of response quality on bias measurement.
Using the DBG score, we conduct comprehensive experiments to assess
self-preference bias across LLMs of varying versions, sizes, and reasoning
abilities. Additionally, we investigate two factors that influence and help
alleviate self-preference bias: response text style and the post-training data
of judge models. Finally, we explore potential underlying mechanisms of
self-preference bias from an attention-based perspective. Our code and data are
available at https://github.com/zhiyuanc2001/self-preference.
684104c89ec96d9991484c24
Zhi-Yuan Chen
extracted_pending
2025-06-05T02:45:29.221Z
65309a1d657ae56cdb65e0e7
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/lHQI9RNjfz8E5v1uyCGeV.png
Zhi-Yuan Chen
user
JaxChen
684104c89ec96d9991484c25
Hao Wang
684104c89ec96d9991484c26
Xinyu Zhang
684104c89ec96d9991484c27
Enrui Hu
684104c89ec96d9991484c28
Yankai Lin
Beyond the Surface: Measuring Self-Preference in LLM Judgments
Recent studies show that large language models (LLMs) exhibit self-preference
bias when serving as judges, meaning they tend to favor their own responses
over those generated by other models. Existing methods typically measure this
bias by calculating the difference between the scores a judge model assigns to
its own responses and those it assigns to responses from other models. However,
this approach conflates self-preference bias with response quality, as
higher-quality responses from the judge model may also lead to positive score
differences, even in the absence of bias. To address this issue, we introduce
gold judgments as proxies for the actual quality of responses and propose the
DBG score, which measures self-preference bias as the difference between the
scores assigned by the judge model to its own responses and the corresponding
gold judgments. Since gold judgments reflect true response quality, the DBG
score mitigates the confounding effect of response quality on bias measurement.
Using the DBG score, we conduct comprehensive experiments to assess
self-preference bias across LLMs of varying versions, sizes, and reasoning
abilities. Additionally, we investigate two factors that influence and help
alleviate self-preference bias: response text style and the post-training data
of judge models. Finally, we explore potential underlying mechanisms of
self-preference bias from an attention-based perspective. Our code and data are
available at https://github.com/zhiyuanc2001/self-preference.
The DBG score is introduced to measure self-preference bias in large language models by using gold judgments as proxies for response quality, addressing the confounding effect of response quality.
large language models self-preference bias judge model gold judgments DBG score response quality attention-based perspective