Automated evaluation using LLM-as-a-Judge has become common, but these systems remain vulnerable to systematic biases that can undermine the trustworthiness of their assessments. A new preprint on arXiv addresses this problem by asking whether the judge itself can correct for such biases.
The authors report that the judge can effectively debias itself without additional—likely meaning without extra data or model changes—though the abstract is truncated and full details are not yet available. This points toward a lightweight, self-contained strategy for improving evaluation reliability.
As a single preprint, the findings have not yet been peer-reviewed, and the abstract does not specify which biases were targeted or how large the improvements were. Still, the idea of self-debiasing could offer a practical path for making LLM-based evaluation more robust.