General Information Metrics for Improving AI Model Training Efficiency

TL;DR

This paper introduces GIME, a universal data selection method based on information theory metrics, which optimizes training datasets to reduce costs and time while maintaining model performance across various domains.

Contribution

The paper proposes GIME, a novel information metrics-based approach for dataset selection that improves training efficiency and reduces costs in AI model development.

Findings

GIME reduces training time and costs significantly.

GIME maintains model performance across diverse tasks.

Application in Judicial AI cut training expenses by nearly 40%.

Abstract

To address the growing size of AI model training data and the lack of a universal data selection methodology-factors that significantly drive up training costs -- this paper presents the General Information Metrics Evaluation (GIME) method. GIME leverages general information metrics from Objective Information Theory (OIT), including volume, delay, scope, granularity, variety, duration, sampling rate, aggregation, coverage, distortion, and mismatch to optimize dataset selection for training purposes. Comprehensive experiments conducted across diverse domains, such as CTR Prediction, Civil Case Prediction, and Weather Forecasting, demonstrate that GIME effectively preserves model performance while substantially reducing both training time and costs. Additionally, applying GIME within the Judicial AI Program led to a remarkable 39.56% reduction in total model training expenses, underscoring its potential to support efficient and sustainable AI development.