Collaborative Performance Prediction for Large Language Models

TL;DR

This paper introduces a novel Collaborative Performance Prediction framework that leverages historical performance data and design factors to improve accuracy in predicting large language model performance across tasks.

Contribution

The paper proposes a new CPP framework that incorporates collaborative data and considers both model and task design factors for enhanced performance prediction.

Findings

CPP surpasses traditional scaling laws in accuracy

Collaborative data improves prediction robustness

Analysis of factor importance reveals new insights

Abstract

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.