Summary of Tinybenchmarks: Evaluating Llms with Fewer Examples, by Felipe Maia Polo et al.
tinyBenchmarks: evaluating LLMs with fewer examples
by Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, Mikhail Yurochkin
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); 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 The paper presents a solution to reduce the computational cost of evaluating large language models (LLMs) on various benchmarks. By identifying a subset of representative examples, researchers can accurately estimate the model’s performance without requiring extensive evaluations. The authors demonstrate this approach using popular benchmarks such as MMLU and release evaluation tools and tiny versions of these datasets. Their empirical analysis shows that these tools enable reliable and efficient reproduction of original evaluation results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier to test how well large language models work. Right now, evaluating these models on many different tasks is very time-consuming because it requires looking at tens of thousands of examples. But the authors found a way to reduce this number to just a few hundred carefully chosen examples. This makes it faster and cheaper to test models and compare their performance. They also share tools that make it easier to do evaluations, which will help researchers reproduce results more quickly. |
