Benchmarking LLM-as-a-Judge for Long-Form Output Evaluation
The paper introduces LongJudgeBench, a benchmark designed to evaluate large language models (LLMs) as judges for long-form outputs. It highlights the challenges of reliably assessing long-form content compared to short-form evaluations. The study reveals significant reliability gaps in current LLM judges and emphasizes the need for more robust evaluation methods.
- ▪LongJudgeBench is a comprehensive benchmark for evaluating LLM judges on long-form outputs.
- ▪Current LLM judges show instability across different scenarios, indicating a reliability gap.
- ▪The evaluation of long-form outputs requires complex assessments beyond just output length.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2606.01629 |
| Publication time | Wed, 03 Jun 2026 04:48:08 +0000 |
| Retrieval time | 2026-06-03T04:51:55.339Z |
| Last seen | 2026-06-03T04:51:55.339Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Opening excerpt (first ~120 words) tap to expand
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 View a PDF of the paper titled Benchmarking LLM-as-a-Judge for Long-Form Output Evaluation, by Junjie Chen and 7 other authors View PDF HTML (experimental) Abstract:As large language models (LLMs) are increasingly used for long-form generation, reliably evaluating long-form outputs has become a critical challenge.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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