arXiv:2509.03647v2 Announce Type: replace-cross Abstract: 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 and model routing. We investigate whether lightweight steering vectors can mitigate this problem at inference time without retraining. We introduce a curated dataset that distinguishes self-preference bias into justified examples of self-pre
The proliferation of LLMs as evaluators in critical pipelines makes addressing their inherent biases an immediate necessity to ensure reliability and fairness.
Sophisticated readers should care because unmitigated self-preference bias in AI evaluators compromises the integrity of AI development cycles and application deployments, potentially leading to suboptimal or unfair outcomes.
The ability to mitigate self-preference bias at inference time without retraining offers a pragmatic and rapid solution to a fundamental issue in LLM evaluation, improving the trustworthiness and efficiency of AI development.
- · AI developers
- · LLM application users
- · AI fairness researchers
- · Model evaluation platforms
- · Models with inherent severe biases
- · Unfair model evaluation methods
The quality and fairness of LLM evaluations improve.
Faster and more reliable iteration cycles for LLM development become possible, accelerating the pace of AI innovation.
Enhanced trust in AI evaluations could lead to broader adoption of LLMs in sensitive decision-making processes, but also new forms of 'goodharting' the evaluation itself.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI