@inproceedings{zhang-etal-2026-compassjudger,
title = "{C}ompass{J}udger-2: A Holistic Approach Towards Generalist Judge Model",
author = "Zhang, Taolin and
Ma, Zihan and
Maosongcao and
Liu, Shudong and
Liu, Junnan and
Zhang, Songyang and
Chen, Kai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1108/",
doi = "10.18653/2026.findings-emnlp.1108",
pages = "23442--23461",
ISBN = "979-8-89176-462-0",
abstract = "Recently, the role of LLM-as-judge in evaluating large language models has gained prominence, emerging as an important method to partially replace costly human assessment. However, current judge models suffer from narrow specialization and limited robustness, undermining their capacity for comprehensive evaluations. In this work, we decompose the ability of a generalist and generative judge model into three levels: objective verification, subjective evaluation, and rubric refinement. We present CompassJudger-2, a novel generalist judge model that overcomes these limitations via a task-driven, multi-scenarios data curation strategy. We conducted large-scale data collection for each type of task and designed tailored rejection sampling strategies to filter the data, ensuring data diversity, accuracy, and effectiveness. Empirically, CompassJudger-2 achieves superior results across multiple judge and reward benchmarks, demostrating its excellent robustness and generalization ability. These contributions advance robust, scalable LLM judgment and establish new performance and evaluation standards."
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%0 Conference Proceedings
%T CompassJudger-2: A Holistic Approach Towards Generalist Judge Model
%A Zhang, Taolin
%A Ma, Zihan
%A Liu, Shudong
%A Liu, Junnan
%A Zhang, Songyang
%A Chen, Kai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Maosongcao
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-compassjudger
%X Recently, the role of LLM-as-judge in evaluating large language models has gained prominence, emerging as an important method to partially replace costly human assessment. However, current judge models suffer from narrow specialization and limited robustness, undermining their capacity for comprehensive evaluations. In this work, we decompose the ability of a generalist and generative judge model into three levels: objective verification, subjective evaluation, and rubric refinement. We present CompassJudger-2, a novel generalist judge model that overcomes these limitations via a task-driven, multi-scenarios data curation strategy. We conducted large-scale data collection for each type of task and designed tailored rejection sampling strategies to filter the data, ensuring data diversity, accuracy, and effectiveness. Empirically, CompassJudger-2 achieves superior results across multiple judge and reward benchmarks, demostrating its excellent robustness and generalization ability. These contributions advance robust, scalable LLM judgment and establish new performance and evaluation standards.
%R 10.18653/2026.findings-emnlp.1108
%U https://aclanthology.org/2026.findings-emnlp.1108/
%U https://doi.org/10.18653/2026.findings-emnlp.1108
%P 23442-23461
Markdown (Informal)
[CompassJudger-2: A Holistic Approach Towards Generalist Judge Model](https://aclanthology.org/2026.findings-emnlp.1108/) (Zhang et al., Findings 2026)
ACL