  Performance prediction is a method to estimate the performance of Language
Models (LMs) on various Natural Language Processing (NLP) tasks, mitigating
computational costs associated with model capacity and data for fine-tuning.
Our paper introduces ProxyLM, a scalable framework for predicting LM
performance using proxy models in multilingual tasks. These proxy models act as
surrogates, approximating the performance of the LM of interest. By leveraging
proxy models, ProxyLM significantly reduces computational overhead on task
evaluations, achieving up to a 37.08x speedup compared to traditional methods,
even with our smallest proxy models. Additionally, our methodology showcases
adaptability to previously unseen languages in pre-trained LMs, outperforming
the state-of-the-art performance by 1.89x as measured by root-mean-square error
(RMSE). This framework streamlines model selection, enabling efficient
deployment and iterative LM enhancements without extensive computational
resources.
