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.

翻译：性能预测是一种用于估计多语言语言模型（LMs）性能的方法，旨在降低与模型容量和微调数据相关的计算成本。本文提出ProxyLM，一个利用代理模型预测语言模型在多语言任务中性能的可扩展框架。这些代理模型作为替代品，能够近似微调后语言模型在特定下游自然语言处理（NLP）任务上的表现。通过利用代理模型，ProxyLM显著降低了任务评估的计算开销，即使使用我们最小的代理模型，相比传统方法也能实现高达37.08倍的加速。此外，我们的方法展示了在预训练语言模型中对未见语言的适应能力，以均方根误差（RMSE）衡量，其性能优于现有最佳方法1.89倍。该框架简化了模型选择过程，使得无需大量计算资源即可实现高效部署和语言模型的迭代改进。