tinyBenchmarks: evaluating LLMs with fewer examples¶
Arxiv Link - 2024-05-26 22:27:23
Abstract¶
The versatility of large language models (LLMs) led to the creation of diverse benchmarks that thoroughly test a variety of language models' abilities. These benchmarks consist of tens of thousands of examples making evaluation of LLMs very expensive. In this paper, we investigate strategies to reduce the number of evaluations needed to assess the performance of an LLM on several key benchmarks. For example, we show that to accurately estimate the performance of an LLM on MMLU, a popular multiple-choice QA benchmark consisting of 14K examples, it is sufficient to evaluate this LLM on 100 curated examples. We release evaluation tools and tiny versions of popular benchmarks: Open LLM Leaderboard, MMLU, HELM, and AlpacaEval 2.0. Our empirical analysis demonstrates that these tools and tiny benchmarks are sufficient to reliably and efficiently reproduce the original evaluation results.
Socials¶
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| 🚀 Exciting news in the world of AI and NLP! A recent study explores strategies to streamline the evaluation of large language models (LLMs) on various benchmarks. By reducing the number of evaluations required, assessing LLM performance becomes more efficient and cost-effective. One striking finding from the research is that evaluating an LLM on just 100 carefully selected examples can accurately estimate its performance on a benchmark with tens of thousands of examples. The study also introduces evaluation tools and compact versions of popular benchmarks like MMLU and HELM. For further insights into this groundbreaking research, check out the full paper at: http://arxiv.org/abs/2402.14992v2 #AI #NLP #LLMs #Research #Tech #Innovation |
🚀 Exciting research on optimizing evaluation of Large Language Models (LLMs)! Find out how to assess LLM performance on key benchmarks more efficiently. Check out the paper here: http://arxiv.org/abs/2402.14992v2 #AI #NLP #LLMs #TechResearch |