Summary of From Generation to Judgment: Opportunities and Challenges Of Llm-as-a-judge, by Dawei Li et al.
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge
by 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
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper surveys the emerging field of Large Language Model (LLM)-based judgment and assessment, where LLMs are utilized for scoring, ranking, or selection across various tasks and applications. The authors provide a comprehensive overview of this paradigm, introducing a taxonomy to explore LLM-as-a-judge from three dimensions: what to judge, how to judge, and where to judge. The paper also compiles benchmarks for evaluating LLM-as-a-judge and highlights key challenges and promising directions. This research aims to advance the field by providing valuable insights and inspiring future studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a new way to use Large Language Models (LLMs) in artificial intelligence and natural language processing. Right now, it’s hard to evaluate things like how well an AI system does something or whether one AI system is better than another. The authors propose using LLMs as judges for these tasks. They also create a framework to help people understand this new approach and provide some examples of how it can be used. This research hopes to make progress in this area by providing more information and inspiring further study. |
Keywords
» Artificial intelligence » Large language model » Natural language processing
