{"slug": "a-survey-on-llm-as-a-judge", "title": "A Survey on LLM-as-a-Judge", "summary": "A comprehensive survey on LLM-as-a-Judge, submitted to arXiv on 23 Nov 2024 and revised through v6 on 19 Oct 2025, addresses how to build reliable LLM-based evaluation systems, proposing strategies to improve consistency, mitigate biases, and adapt to diverse scenarios, along with a novel benchmark for assessing reliability. The paper, authored by Xuhui Jiang and colleagues, serves as a foundational reference for researchers and practitioners in the rapidly evolving field.", "body_md": "# Computer Science > Computation and Language\n\n[Submitted on 23 Nov 2024 (\n\n[v1](https://arxiv.org/abs/2411.15594v1)), last revised 19 Oct 2025 (this version, v6)]# Title:A Survey on LLM-as-a-Judge\n\n[View PDF](/pdf/2411.15594)\n\n[HTML (experimental)](https://arxiv.org/html/2411.15594v6)\n\nAbstract:Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains a challenging task due to inherent subjectivity, variability, and scale. Large Language Models (LLMs) have achieved remarkable success across diverse domains, leading to the emergence of \"LLM-as-a-Judge,\" where LLMs are employed as evaluators for complex tasks. With their ability to process diverse data types and provide scalable, cost-effective, and consistent assessments, LLMs present a compelling alternative to traditional expert-driven evaluations. However, ensuring the reliability of LLM-as-a-Judge systems remains a significant challenge that requires careful design and standardization. This paper provides a comprehensive survey of LLM-as-a-Judge, addressing the core question: How can reliable LLM-as-a-Judge systems be built? We explore strategies to enhance reliability, including improving consistency, mitigating biases, and adapting to diverse assessment scenarios. Additionally, we propose methodologies for evaluating the reliability of LLM-as-a-Judge systems, supported by a novel benchmark designed for this purpose. To advance the development and real-world deployment of LLM-as-a-Judge systems, we also discussed practical applications, challenges, and future directions. This survey serves as a foundational reference for researchers and practitioners in this rapidly evolving field.\n\n## Submission history\n\nFrom: Xuhui Jiang [[view email](/show-email/deaaef6f/2411.15594)]\n\n**Sat, 23 Nov 2024 16:03:35 UTC (1,888 KB)**\n\n[[v1]](/abs/2411.15594v1)**Mon, 16 Dec 2024 15:00:53 UTC (2,820 KB)**\n\n[[v2]](/abs/2411.15594v2)**Thu, 9 Jan 2025 03:08:17 UTC (1,477 KB)**\n\n[[v3]](/abs/2411.15594v3)**Sat, 1 Feb 2025 08:55:51 UTC (10,153 KB)**\n\n[[v4]](/abs/2411.15594v4)**Sun, 9 Mar 2025 05:21:22 UTC (13,276 KB)**\n\n[[v5]](/abs/2411.15594v5)**[v6]** Sun, 19 Oct 2025 10:32:43 UTC (26,593 KB)\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/a-survey-on-llm-as-a-judge", "canonical_source": "https://arxiv.org/abs/2411.15594", "published_at": "2026-08-05 16:27:49+00:00", "updated_at": "2026-08-05 16:37:17.212660+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "ai-ethics"], "entities": ["arXiv", "Xuhui Jiang"], "alternates": {"html": "https://wpnews.pro/news/a-survey-on-llm-as-a-judge", "markdown": "https://wpnews.pro/news/a-survey-on-llm-as-a-judge.md", "text": "https://wpnews.pro/news/a-survey-on-llm-as-a-judge.txt", "jsonld": "https://wpnews.pro/news/a-survey-on-llm-as-a-judge.jsonld"}}