arxiv preprint - tinyBenchmarks: evaluating LLMs with fewer examples

In this episode, we discuss tinyBenchmarks: evaluating LLMs with fewer examples by Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, Mikhail Yurochkin. The paper discusses strategies to minimize the number of evaluations required to effectively assess the performance of large language models on major benchmarks. By analyzing a popular QA benchmark called MMLU, the authors demonstrate that evaluating a language model on merely 100 well-chosen examples can yield an accurate estimate of its performance. The authors have developed and released evaluation tools and condensed versions of benchmarks including Open LLM Leaderboard, MMLU, HELM, and AlpacaEval 2.0, which have been empirically shown to reliably replicate the outcomes of the original expansive evaluations.