Summary of Active Evaluation Acquisition For Efficient Llm Benchmarking, by Yang Li et al.
Active Evaluation Acquisition for Efficient LLM Benchmarking
by Yang Li, Jie Ma, Miguel Ballesteros, Yassine Benajiba, Graham Horwood
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 newly proposed strategy is designed to efficiently evaluate the capabilities of large language models (LLMs) by selecting a subset of examples from existing benchmarks. This approach uses learned policies to model dependencies across test examples, allowing for accurate prediction of evaluation outcomes based on the selected ones. By only acquiring actual evaluation outcomes for the chosen subset, computation costs can be significantly reduced without compromising performance estimates. The effectiveness of this method is demonstrated through rigorous exploration of various subset selection policies and the introduction of a novel RL-based policy that leverages captured dependencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting smarter, but we need ways to test their abilities without using too many computer resources or taking up too much time. To solve this problem, scientists have developed a new way to pick just the right examples from large tests to make sure they get an accurate picture of how well these models are performing. This approach uses special rules to understand how different examples relate to each other and then picks the ones that will give the most useful information. By only looking at these chosen examples, we can save time and money while still getting a good idea of how well these language models work. |
