Comprehensively understanding and accurately predicting the performance of large language models across diverse downstream tasks has emerged as a pivotal challenge in NLP research. The pioneering scaling law on downstream works demonstrated intrinsic similarities within model families and utilized such similarities for performance prediction. However, they tend to overlook the similarities between model families and only consider design factors listed in the original scaling law. To overcome these limitations, we introduce a novel framework, Collaborative Performance Prediction (CPP), which significantly enhances prediction accuracy by leveraging the historical performance of various models on downstream tasks and other design factors for both model and task. We also collect a collaborative data sourced from online platforms containing both historical performance and additional design factors. With the support of the collaborative data, CPP not only surpasses traditional scaling laws in predicting the performance of scaled LLMs but also facilitates a detailed analysis of factor importance, an area previously overlooked.

通过利用历史性能和其他设计因素, 我们引入了一种新的框架Collaborative Performance Prediction (CPP)来显著提高性能预测准确性, 该框架不仅超越了传统的缩放法则在预测扩展LLMs的性能方面,还促进了对因素重要性的详细分析, 在NLP研究中全面理解和准确预测大规模语言模型在各种下游任务中的表现已成为一个关键性的挑战。

大型语言模型的协同性能预测