Performance prediction is a method to estimate the performance of
multilingual language models (LMs), 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 fine-tuned LMs on specific downstream natural language
processing (NLP) tasks. 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.

ProxyLM 是一种可扩展的框架，通过使用代理模型在多语言任务中预测语言模型性能，减少与模型容量和微调数据相关的计算成本，大大降低了任务评估的计算负荷，同时展示了适应预训练语言模型中以前未见过的语言，提高了性能，并通过简化模型选择实现了高效部署和迭代式语言模型增强。