[Submitted on 1 Jun 2026 (
[v1](https://arxiv.org/abs/2606.01629v1)), last revised 2 Jun 2026 (this version, v2)]# Title:Benchmarking LLM-as-a-Judge for Long-Form Output Evaluation
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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].
Submission history #
From: Junjie Chen [[view email](/show-email/b56a0611/2606.01629)]
**Mon, 1 Jun 2026 03:25:34 UTC (325 KB)**
[[v1]](/abs/2606.01629v1)**[v2]** Tue, 2 Jun 2026 07:49:40 UTC (327 KB)
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