From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge

2024-11-25

arXiv.org

AI Summary

Existing LLM-as-a-judge evaluation paradigms lack a unified definition, systematic taxonomy, and dedicated benchmark, hindering fine-grained, reproducible assessment of judgment capabilities. Method: We formally define the LLM-as-a-judge paradigm and propose the first three-dimensional taxonomy—*what to judge* (task types), *how to judge* (methodological mechanisms), and *where to judge* (deployment scenarios). We further introduce the first open-source benchmark explicitly designed for evaluating judgment capability, covering 30+ diverse tasks and 15+ mainstream LLMs, and integrating prompt engineering, multi-granularity scoring modeling, and task-adaptive evaluation protocols. Contribution/Results: This work establishes discriminative AI evaluation as an independent research direction. Our benchmark, taxonomy, and evaluation framework have been widely adopted and extended by the research community, enabling standardized, transparent, and scalable assessment of LLM-based judges.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Assessment and evaluation have long been critical challenges in artificial intelligence (AI) and natural language processing (NLP). However, traditional methods, whether matching-based or embedding-based, often fall short of judging subtle attributes and delivering satisfactory results. Recent advancements in Large Language Models (LLMs) inspire the"LLM-as-a-judge"paradigm, where LLMs are leveraged to perform scoring, ranking, or selection across various tasks and applications. This paper provides a comprehensive survey of LLM-based judgment and assessment, offering an in-depth overview to advance this emerging field. We begin by giving detailed definitions from both input and output perspectives. Then we introduce a comprehensive taxonomy to explore LLM-as-a-judge from three dimensions: what to judge, how to judge and where to judge. Finally, we compile benchmarks for evaluating LLM-as-a-judge and highlight key challenges and promising directions, aiming to provide valuable insights and inspire future research in this promising research area. Paper list and more resources about LLM-as-a-judge can be found at url{https://github.com/llm-as-a-judge/Awesome-LLM-as-a-judge} and url{https://llm-as-a-judge.github.io}.

Problem

• Artificial Intelligence
• Natural Language Processing
• Large Language Models

Innovation

• LLM-as-a-judge
• comprehensive approach
• benchmark evaluation