ArticleInnovation (Cambridge (Mass.))2026
A survey on LLM-as-a-judge.
Article in Innovation (Cambridge (Mass.)), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed.
- Code-driven planning in grid worlds with large language models.Royal Society open science · 2026Article
- What Platform Scores Miss: Multidimensional Evaluation of AI Teaching Agents in Medical Education.JMIR medical education · 2026Article
- Large Language Models in the Radiology Workflow: Current Applications, Challenges, and Future Perspectives.Journal of the Korean Society of Radiology · 2026Review
- Foundation models in medical image analysis: A systematic review and quantitative analysis.Medical image analysis · 2026Review
- Multiagent Large Language Model Framework for Psychotherapy Fidelity Assessment in Motivational Interviewing and Cognitive Behavioral Therapy Training: Cross-Sectional, Simulation-Based Evaluation Study.JMIR medical education · 2026Article
- Effect of evaluation prompt strategies on LLM-as-a-judge reliability in critical care.Anaesthesiology intensive therapy · 2026Article
- MetaboT: an LLM-based multi-agent framework for interactive analysis of mass spectrometry metabolomics knowledge graphs.Journal of cheminformatics · 2026Article
- Hepatology e-consult responses generated by artificial intelligence demonstrate accuracy but require human oversight.Hepatology communications · 2026Article
- WaterRAG: A Multiagent Retrieval-Augmented Generation Framework to Support Water Industry Transitions to Net-Zero.Environmental science & technology · 2026Article
- PsychiatryBench: a multi-task benchmark for LLMs in psychiatry.NPJ digital medicine · 2026Article
- Protocol deviation outlier estimation combined with generative AI.BMC research notes · 2026Article
- A conversational multi-agent AI system for automated plant phenotyping.Nature communications · 2026Article
- Can Small Open-Source Language Models With Retrieval-Augmented Generation Match GPT-4 Performance in Breast Cancer Clinical Decision Support?JCO clinical cancer informatics · 2026Article
- Evaluating LLMs' divergent thinking capabilities for scientific idea generation with minimal context.Nature communications · 2026Article
- Benchmarking Large Language Models on the Taiwan Neurology Board Examinations (2018-2024): A Comparative Evaluation of GPT-4o, GPT-o1, DeepSeek-V3, and DeepSeek-R1.Bioengineering (Basel, Switzerland) · 2026Article
- Language models reveal evidence gaps in variants of uncertain significance.medRxiv : the preprint server for health sciences · 2026Article
- Advancing the science of qualitative patient preference assessment using large language models.PLOS digital health · 2026Article
- Article
- MediQAl: A French Medical Question Answering Dataset for Knowledge and Reasoning Evaluation.Scientific data · 2026Article
- Attempts to investigate what words trivia experts focus on in quiz questions: Human-AI comparison through "LLM-as-a-judge" approach.PloS one · 2026Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains challenging due to inherent subjectivity, variability, and scale. Large language models (LLMs) have achieved remarkable success, leading to "LLM-as-a-judge," where LLMs serve as evaluators for complex tasks. With their ability to process diverse data types and provide scalable 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 requiring careful design and standardization. This paper provides a comprehensive survey of LLM-as-a-judge, offering a formal definition and detailed classification while addressing the core question of how to build reliable LLM-as-a-judge systems. We explore strategies to enhance reliability, including improving consistency, mitigating biases, and adapting to diverse scenarios. We propose methodologies for evaluating reliability, supported by a novel benchmark. To advance development and deployment, we discuss practical applications, challenges, and future directions. Our contributions span multiple levels: we establish conceptual boundaries, reorganize fragmented literature into a unified framework, and propose a reliability-oriented benchmark. We articulate a forward-looking research agenda, offering theoretical foundations and practical guidance for constructing reliable and trustworthy LLM-as-a-judge systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.