@inproceedings{liu-etal-2026-judgemoe,
title = "{J}udge{M}o{E}: Distributional Aggregation for {LLM}-as-a-Judge",
author = "Liu, Yiqi and
James, Joseph and
Wang, Yang and
Zhao, Kun and
Xiao, Chenghao and
Lin, Chenghua",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.49/",
pages = "1016--1042",
ISBN = "979-8-89176-439-2",
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."
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%0 Conference Proceedings
%T JudgeMoE: Distributional Aggregation for LLM-as-a-Judge
%A Liu, Yiqi
%A James, Joseph
%A Wang, Yang
%A Zhao, Kun
%A Xiao, Chenghao
%A Lin, Chenghua
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F liu-etal-2026-judgemoe
%X 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.
%U https://aclanthology.org/2026.aacl-long.49/
%P 1016-1042
Markdown (Informal)
[JudgeMoE: Distributional Aggregation for LLM-as-a-Judge](https://aclanthology.org/2026.aacl-long.49/) (Liu et al., AACL-IJCNLP 2026)
ACL
- Yiqi Liu, Joseph James, Yang Wang, Kun Zhao, Chenghao Xiao, and Chenghua Lin. 2026. JudgeMoE: Distributional Aggregation for LLM-as-a-Judge. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1016–1042, Hengqin, China. Association for Computational Linguistics.