Paper · arXiv 2410.13804
BenTo: Benchmark Task Reduction with In-Context Transferability
Hongyu Zhao, Ming Li, Lichao Sun, Tianyi Zhou
Efficiently reducing the number of tasks for evaluating large language models with minimal impact on evaluation quality using a transferability metric based on in-context learning.
Abstract
Evaluating large language models (LLMs) is costly: it requires the generation and examination of LLM outputs on a large-scale benchmark of various tasks. This paper investigates how to efficiently reduce the tasks used to benchmark LLMs without affecting the evaluation quality. Our study reveals that task transferability and relevance provide critical information to identify the most representative subset of tasks via optimizing a facility location function. We propose a practically efficient metric for estimating the transferability between two tasks via in-context learning (ICL). By analyzing the pairwise transferability, we can reduce tasks in a modern LLM benchmark (e.g., MMLU or FLAN) to 5% while inducing only a <4% difference to the evaluation on the original benchmark. Compared to prior works, our method is training-free, gradient-free, and highly efficient requiring ICL only.