Collaborative Performance Prediction for Large Language Models
This work addresses the problem of performance prediction for NLP researchers and practitioners, offering an incremental improvement by incorporating cross-family similarities and additional factors.
The paper tackles the challenge of predicting large language model performance across diverse tasks by introducing the Collaborative Performance Prediction (CPP) framework, which leverages historical performance and design factors to significantly enhance accuracy compared to traditional scaling laws.
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.