[2606.01629] Benchmarking LLM-as-a-Judge for Long-Form Output Evaluation

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Computer Science > Computation and Language

arXiv:2606.01629 (cs)

[Submitted on 1 Jun 2026 (v1), last revised 2 Jun 2026 (this version, v2)]

Title:Benchmarking LLM-as-a-Judge for Long-Form Output Evaluation

Authors:Junjie Chen, Yuxi Dong, Haitao Li, Weihang Su, Yujia Zhou, Min Zhang, Yiqun Liu, Qinyao Ai<br>View a PDF of the paper titled Benchmarking LLM-as-a-Judge for Long-Form Output Evaluation, by Junjie Chen and 7 other authors

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Abstract:As large language models (LLMs) are increasingly used for long-form generation, reliably evaluating long-form outputs has become a critical challenge. LLM-as-a-judge offers a scalable alternative to human evaluation, yet its reliability in long-form output evaluation remains underexamined: existing meta-evaluation benchmarks focus mainly on short-form outputs. Compared with short-form evaluation, long-form evaluation is not merely a matter of output length; it often requires judges to make more complex document-level assessments of overall organization, task-relevant coverage and depth, cross-section consistency, and scenario-specific quality criteria. In this work, we introduce LongJudgeBench, a comprehensive benchmark for evaluating LLM judges on long-form outputs across diverse real-world scenarios and judging protocols. We systematically evaluate a broad range of LLM judges, covering multiple base models and judging settings. Our results reveal a substantial reliability gap: current LLM judges remain unstable across scenarios, and rubrics or references are helpful but not always sufficient. We hope LongJudgeBench will support future research on more robust, context-aware, and human-aligned LLM-as-a-judge methods. Our code is available at this https URL.

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Computation and Language (cs.CL)

Cite as:<br>arXiv:2606.01629 [cs.CL]

(or<br>arXiv:2606.01629v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.01629

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arXiv-issued DOI via DataCite

Submission history<br>From: Junjie Chen [view email]<br>[v1]<br>Mon, 1 Jun 2026 03:25:34 UTC (325 KB)

[v2]<br>Tue, 2 Jun 2026 07:49:40 UTC (327 KB)

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