Summary of Justice or Prejudice? Quantifying Biases in Llm-as-a-judge, by Jiayi Ye et al.
Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge
by Jiayi Ye, Yanbo Wang, Yue Huang, Dongping Chen, Qihui Zhang, Nuno Moniz, Tian Gao, Werner Geyer, Chao Huang, Pin-Yu Chen, Nitesh V Chawla, Xiangliang Zhang
First submitted to arxiv on: 3 Oct 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 The paper investigates potential biases in LLM-as-a-Judge, a widely used evaluation method in various benchmarks and model training. Despite its excellence in many domains, 12 key biases are identified, which undermine its reliability and utility. A new framework, CALM, is proposed to quantify and analyze each bias type. Experiments with popular language models show that while advanced models perform well overall, significant biases persist in specific tasks. The results suggest room for improvement in LLM-as-a-Judge reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a way people evaluate AI models called LLM-as-a-Judge. They found 12 ways this method can be unfair and might not give the best results. To fix this, they created a new tool that helps spot these biases. The tool showed that even very good AI models have these problems in certain situations. This means we need to work on making sure LLM-as-a-Judge is fair and reliable. |
