  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.
