{"work":{"id":"2676656a-67bd-4ad5-bad6-cb6f5fcdbfbe","openalex_id":null,"doi":null,"arxiv_id":"2411.15594","raw_key":null,"title":"A Survey on LLM-as-a-Judge","authors":null,"authors_text":"Jiawei Gu, Xuhui Jiang, Zhichao Shi, Hexiang Tan, Xuehao Zhai, Chengjin Xu, Wei Li, Yinghan Shen, Shengjie Ma, Honghao Liu, Saizhuo Wang, Kun Zhang, Yuanzhuo Wang, Wen Gao, Lionel Ni, and Jian Guo","year":2024,"venue":"cs.CL","abstract":"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.","external_url":"https://arxiv.org/abs/2411.15594","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-05-15T01:33:26.886266+00:00","pith_arxiv_id":"2411.15594","created_at":"2026-05-08T21:39:24.641446+00:00","updated_at":"2026-05-15T01:33:26.886266+00:00","title_quality_ok":false,"display_title":"A Survey on LLM-as-a-Judge","render_title":"A Survey on LLM-as-a-Judge"},"hub":{"state":{"work_id":"2676656a-67bd-4ad5-bad6-cb6f5fcdbfbe","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":65,"external_cited_by_count":null,"distinct_field_count":13,"first_pith_cited_at":"2025-02-26T06:17:13+00:00","last_pith_cited_at":"2026-05-14T08:38:04+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-05-15T01:46:18.732976+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":2}],"polarity_counts":[{"context_polarity":"background","n":2}],"runs":{"context_extract":{"job_type":"context_extract","status":"succeeded","result":{"enqueued_papers":25},"error":null,"updated_at":"2026-05-14T10:49:17.018350+00:00"},"graph_features":{"job_type":"graph_features","status":"succeeded","result":{"co_cited":[{"title":"Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena","work_id":"d0c30cd7-81e1-4159-a87f-f6adca77ff08","shared_citers":9},{"title":"LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods","work_id":"377b190f-39ed-4db3-9747-433802e5303c","shared_citers":8},{"title":"Qwen3 Technical Report","work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e","shared_citers":8},{"title":"GPT-4 Technical Report","work_id":"b928e041-6991-4c08-8c81-0359e4097c7b","shared_citers":7},{"title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","work_id":"c5006563-f3ec-438a-9e35-b7b484f34828","shared_citers":6},{"title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","work_id":"e6b75ad5-2877-4168-97c8-710407094d20","shared_citers":6},{"title":"LLaMA: Open and Efficient Foundation Language Models","work_id":"c018fc23-6f3f-4035-9d02-28a2173b2b9d","shared_citers":6},{"title":"Evaluating Large Language Models Trained on Code","work_id":"042493e9-b26f-4b4e-bbde-382072ca9b08","shared_citers":5},{"title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","work_id":"8c6d5a6b-b5cc-4105-9c84-9c34bb9375bb","shared_citers":5},{"title":"The Llama 3 Herd of Models","work_id":"1549a635-88af-4ac1-acfe-51ae7bb53345","shared_citers":5},{"title":"Training Verifiers to Solve Math Word Problems","work_id":"acab1aa8-b4d6-40e0-a3ee-25341701dca2","shared_citers":5},{"title":"Constitutional AI: Harmlessness from AI Feedback","work_id":"faaaa4e0-2676-4fac-a0b4-99aef10d2095","shared_citers":4},{"title":"H., Chen, S., Liu, Z., Jiang, F., and Wang, B","work_id":"f26248cc-fd46-4eb4-94cf-a455fa482305","shared_citers":4},{"title":"Holistic Evaluation of Language Models","work_id":"cc02a01e-7218-47dc-8e66-3333e7e4adec","shared_citers":4},{"title":"On the Opportunities and Risks of Foundation Models","work_id":"a18039e9-928d-47c9-a836-32656a71bf71","shared_citers":4},{"title":"OpenAI o1 System Card","work_id":"68d3c334-0fc9-49e3-b7b0-a69afae933e2","shared_citers":4},{"title":"Reflexion: Language Agents with Verbal Reinforcement Learning","work_id":"778f739e-5f55-4961-8a2a-e4736a2757f4","shared_citers":4},{"title":"s1: Simple test-time scaling","work_id":"806265b1-8f22-48dd-b8ad-a99823b18fa4","shared_citers":4},{"title":"Scaling Laws for Neural Language Models","work_id":"b7dd8749-9c45-4977-ab9b-64478dce1ae8","shared_citers":4},{"title":"arXiv preprint arXiv:2305.15324 , year=","work_id":"12dff4e7-a81d-4b95-aba7-885a457042c6","shared_citers":3},{"title":"arXiv preprint arXiv:2412.12509 , year =","work_id":"fbc020bc-197e-43ce-92fd-3dca29743d27","shared_citers":3},{"title":"DeepSeek-V3 Technical Report","work_id":"57d2791d-2219-4c31-a077-afc04b12a75c","shared_citers":3},{"title":"Gemini: A Family of Highly Capable Multimodal Models","work_id":"83f7c85b-3f11-450f-ac0c-64d9745220b2","shared_citers":3},{"title":"Justice or prejudice? 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