Summary of Limits to Scalable Evaluation at the Frontier: Llm As Judge Won’t Beat Twice the Data, by Florian E. Dorner et al.
Limits to scalable evaluation at the frontier: LLM as Judge won’t beat twice the data
by Florian E. Dorner, Vivian Y. Nastl, Moritz Hardt
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A scalable evaluation method is crucial to keep pace with the rapid growth of machine learning. Existing strong models can be used as judges to reduce annotation costs, but this approach introduces biases like self-preferencing that distort model comparisons. Debiasing tools using high-quality labels aim to fix these issues, but how far can they really go? Our study investigates the theoretical limits of debiasing methods and finds that when the judge is no more accurate than the evaluated model, no method can reduce label requirements by more than half. We demonstrate this limit through empirical evaluation, highlighting the challenges of using LLMs as judges to assess newly released models. Debiasing methods for model evaluation are also explored, revealing promising avenues for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is getting better and faster, but it’s hard to keep track! To make it easier, we need ways to test new models without having to label lots of data. Some people think using strong models as “judges” can help, but this has its own problems. In this paper, the authors look at how well these debiasing methods work and find some big limits. They show that even with great debiasing tools, we might not be able to save much time or effort. The authors also explore what’s next for making model evaluation better. |
Keywords
* Artificial intelligence * Machine learning
