Verified65% confidenceFactExact time
LLM as Judge was systematically proposed by Lianmin Zheng et al. in the MT-Bench paper (2023), with the core idea of using an independent large language model to score the output of another model as a substitute for human annotation.
3
Sources
65%
Confidence
Long-term
Relevance
7/7/2026
First Seen
Sources
Loop Engineering: The New Paradigm for AI Self-Evolution
bilibili镇渊AI6/30/2026
Related Claims
UnverifiedLLM-as-a-Judge is an evaluation paradigm systematically proposed by Zheng et al. in 2023, which uses a more capable model to score or rank the outputs of the model being tested.82% similarUnverifiedThe LLM-as-Judge method was systematically validated by Zheng et al. in the MT-Bench paper, using an independent language model to score target model outputs across multiple dimensions, but it suffers from positional bias and egocentric bias.81% similarUnverified「LLM-as-Judge」范式最早在2023年被系统性提出，利用大语言模型本身的评估能力对生成内容进行质量打分75% similarUnverifiedThe core idea of LLM-as-a-judge is to use a more capable large language model (typically GPT-4, Claude, etc.) to score or rank the outputs of another model.73% similarUnverifiedThe research objective of the RFCLLM paper is to examine the degree of alignment between the implicit representations formed by LLMs for finite state transition systems defined by natural language descriptions and manually constructed ground-truth models.68% similar
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