AI Research
Self- and Other-Labels Induce Bidirectional Bias in LLM Judges
New research exposes how perceived authorship triggers biased scoring in AI-as-a-judge frameworks.
As LLM-as-a-judge systems become increasingly widespread, self-preference in LLMs -- the tendency to favor one's own outputs -- raises growing concerns about evaluation reliability. However, it has been studied predominantly on generated text, where stylistic features and respons...
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ArXiv CS.CL
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ArXiv CS.CL
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This research reveals that AI evaluation bias is often driven by perceived authorship rather than inherent stylistic preference. By isolating attribution from content quality, the study provides a new methodology for developers to build more objective and reliable automated assessment systems.
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