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Breaking the Mirror: Activation-Based Mitigation of Self-Preference in LLM Evaluators
Large language models (LLMs) increasingly serve as automated evaluators, yet they suffer from "self-preference bias": a tendency to favor their own outputs over those of other models. This bias undermines fairness and reliability in evaluation pipelines, particularly for tasks like preference tuning
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