mlsanityData to 14 Oct 2026

Evaluating the Reliability and Fidelity of Automated Judgment Systems of Large Language Models

arXiv 2603.22214 ↗First posted 23 Mar 2026cs.CR cs.AI cs.LGcited by 0

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Abstract (arXiv)

A Large Language Model (LLM) as judge evaluates the quality of victim Machine Learning (ML) models, specifically LLMs, by analyzing their outputs. An LLM as judge is the combination of one model and one specifically engineered judge prompt that contains the criteria for the analysis. The resulting automation of the analysis scales up the complex evaluation of the victim models' free-form text outputs by faster and more consistent judgments compared to human reviewers. Thus, quality and security assessments of LLMs can cover a wide range of the victim models' use cases. Being a comparably new technique, LLMs as judges lack a thorough investigation for their reliability and agreement to human judgment. Our work evaluates the applicability of LLMs as automated quality assessors of victim LLMs. We test the efficacy of 37 differently sized conversational LLMs in combination with 5 different judge prompts, the concept of a second-level judge, and 5 models fine-tuned for the task as assessors. As assessment objective, we curate datasets for eight different categories of judgment tasks and the corresponding ground-truth labels based on human assessments. Our empirical results show a high correlation of LLMs as judges with human assessments, when combined with a suitable prompt, in particular for GPT-4o, several open-source models with geqslant 32B parameters, and a few smaller models like Qwen2.5 14B.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 66 references that are in the map. First posted on arXiv 23 Mar 2026.

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27 of 66 references.

References · 27

  1. 2017Attention Is All You Needcited by 60,361
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  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
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  11. 2023Large Language Models are not Fair Evaluatorscited by 1,085
  12. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 1,017
15 more
  1. 2024Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Modelscited by 446
  2. 2019MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distancecited by 418
  3. 2024Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attackcited by 390
  4. 2022Complexity-Based Prompting for Multi-Step Reasoningcited by 388
  5. 2023JudgeLM: Fine-tuned Large Language Models are Scalable Judgescited by 367
  6. 2024JudgeBench: A Benchmark for Evaluating LLM-based Judgescited by 318
  7. 2023PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimizationcited by 316
  8. 2024R-Judge: Benchmarking Safety Risk Awareness for LLM Agentscited by 288
  9. 2024JailBreakV: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attackscited by 230
  10. 2024Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judgescited by 205
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  12. 2024Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmeticcited by 112
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  15. 2023SocREval: Large Language Models with the Socratic Method for Reference-Free Reasoning Evaluationcited by 27

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