From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge
Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu
2024-11-26
Summary
This paper explores the concept of using Large Language Models (LLMs) as judges to evaluate and assess various tasks, highlighting both the opportunities and challenges in this emerging field.
What's the problem?
Evaluating AI systems and their outputs has always been difficult because traditional methods often fail to capture subtle details and provide accurate assessments. This is especially true for complex tasks where the quality of the output matters significantly. There is a need for better evaluation techniques that can understand and judge these outputs effectively.
What's the solution?
The authors propose the 'LLM-as-a-judge' approach, which utilizes LLMs to perform scoring, ranking, or selection tasks. They provide a detailed overview of how these models can be used to judge various inputs by defining what to judge, how to judge, and where to apply these judgments. The paper also discusses benchmarks for evaluating LLMs in this role and identifies key challenges that need to be addressed, such as ensuring accuracy and transparency in the judgment process.
Why it matters?
This research is important because it paves the way for more reliable and sophisticated evaluation methods in AI. By enhancing how we assess AI outputs, we can improve the overall effectiveness of AI systems in real-world applications. Additionally, understanding how LLMs can serve as judges could lead to advancements in AI fairness, safety, and alignment with human values.
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 https://github.com/llm-as-a-judge/Awesome-LLM-as-a-judge and https://llm-as-a-judge.github.io.