JudgeMoE: Distributional Aggregation for LLM-as-a-Judge
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Computer Science > Computation and Language
arXiv:2610.07109 (cs)
[Submitted on 5 Oct 2026]
Title:JudgeMoE: Distributional Aggregation for LLM-as-a-Judge
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Abstract:When an LLM judge scores an output, its score distribution retains uncertainty and disagreement information that is lost after scalar compression. We introduce JudgeMoE, a lightweight aggregator that assigns example-specific weights to cached judge score distributions and fuses them before computing a final score. A protocol study shows that score-range choice is unstable across judge--dataset settings and that soft scoring usually outperforms hard decoding. On the original 10-cell benchmark, JudgeMoE improves mean Spearman over uniform log pooling by $+0.079$. Applying the same configuration to six additional cells yields a $+0.0393$ mean gain over the strongest local single judge across 16 cells, with positive differences in 12/16 cells and a one-sided Wilcoxon signed-rank $p=0.0091$. Validation-based analyses further show that the preferred aggregation method depends on the task and judge pool.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.07109 [cs.CL] |
| (or arXiv:2610.07109v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07109
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled JudgeMoE: Distributional Aggregation for LLM-as-a-Judge, by Yiqi Liu and 5 other authors
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