# On Speeding Up Language Model Evaluation Source: [On Speeding Up Language Model Evaluation]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:11:54.790Z Authors: Jin Peng Zhou, Christian K. Belardi, Ruihan Wu, Travis Zhang, Carla P. Gomes, Wen Sun, Kilian Q. Weinberger Published: 2024-07-08T17:48:42.000Z ## Abstract Developing prompt-based methods with Large Language Models (LLMs) requires making numerous decisions, which give rise to a combinatorial search problem over hyper-parameters. This exhaustive evaluation can be time-consuming and costly. In this paper, we propose an $\\textit{adaptive}$ approach to explore this space. We are exploiting the fact that often only few samples are needed to identify clearly superior or inferior settings, and that many evaluation tests are highly correlated. We lean on multi-armed bandits to sequentially identify the next (method, validation sample)-pair to evaluate and utilize low-rank matrix factorization to fill in missing evaluations. We carefully assess the efficacy of our approach on several competitive benchmark problems and show that it can identify the top-performing method using only 5-15% of the typical resources -- resulting in 85-95% LLM cost savings. Our code is available at https://github.com/kilian-group/banditeval. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Best Model Identification on 15 datasets aggregate (overall)](): Best Model Identification; 15 datasets aggregate (overall); Mean 90% CB: 19. - [Model Selection on 15 datasets (average)](): Model Selection; 15 datasets (average); Identification Accuracy (8% Budget): 47.2. ## Original sources and implementation [Original paper]() [PDF]()