Summary of A Survey on Llm-as-a-judge, by Jiawei Gu et al.
A Survey on LLM-as-a-Judge
by Jiawei Gu, Xuhui Jiang, Zhichao Shi, Hexiang Tan, Xuehao Zhai, Chengjin Xu, Wei Li, Yinghan Shen, Shengjie Ma, Honghao Liu, Saizhuo Wang, Kun Zhang, Yuanzhuo Wang, Wen Gao, Lionel Ni, Jian Guo
First submitted to arxiv on: 23 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 explores the potential of Large Language Models (LLMs) as evaluators for complex tasks, known as “LLM-as-a-Judge.” LLMs have shown remarkable success across various domains due to their ability to process diverse data types and provide scalable, cost-effective, and consistent assessments. However, ensuring the reliability of LLM-as-a-Judge systems is a significant challenge that requires careful design and standardization. The paper provides a comprehensive survey of LLM-as-a-Judge, discussing strategies to enhance reliability, including improving consistency, mitigating biases, and adapting to diverse assessment scenarios. It also proposes methodologies for evaluating the reliability of LLM-as-a-Judge systems and presents a novel benchmark designed for this purpose. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can be used to help make decisions in many fields like medicine, finance, or education. Large Language Models are special computer programs that can understand and process lots of different types of information. They’re really good at giving consistent ratings and assessments, which makes them a great alternative to human experts. However, it’s important to ensure that these computer systems are reliable and trustworthy. The paper explores ways to make sure LLMs are doing their job accurately, and also proposes new methods for testing the reliability of these systems. |
