Self-Preference Bias in Rubric-Based Evaluation of Large Language Models
LLM-as-a-judge has become the de facto approach for evaluating LLM outputs. However, judges are known to exhibit self-preference bias (SPB): they tend to favor outputs produced by themselves or by models from their own family. This skews evaluations and, thus, hinders model development, especially in settings of recursive self-improvement. We present the first study of SPB in rubric-based evaluation, an increasingly popular benchmarking paradigm where judges issue binary verdicts on individual evaluation criteria, instead of assigning holistic scores or rankings. Using IFEval and LiveCodeBench, benchmarks with programmatically verifiable rubrics, we show that SPB persists even when evalua...
| Authors | José Pombal, Ricardo Rei, André F. T. Martins |
|---|---|
| Primary category | cs.CL |
| Published | 8 April 2026 |
| Last updated at source | 3 August 2026 |
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