Crafting Efficient Fine-Tuning Strategies for Large Language Models

TL;DR

This paper presents a data-efficient and hyperparameter-optimized fine-tuning approach for large language models, achieving high accuracy with minimal data and early performance indicators to reduce computational costs.

Contribution

It introduces a novel Bayesian hyperparameter optimization method that predicts final performance from early training stages, improving fine-tuning efficiency for LLMs.

Findings

Fine-tuning with 200 samples improves accuracy from 70% to 88%.

Diminishing returns observed beyond 6,500 samples.

Early performance evaluation correlates strongly with final results.

Abstract

This paper addresses the challenges of efficiently fine-tuning large language models (LLMs) by exploring data efficiency and hyperparameter optimization. We investigate the minimum data required for effective fine-tuning and propose a novel hyperparameter optimization method that leverages early-stage model performance. Our experiments demonstrate that fine-tuning with as few as 200 samples can improve model accuracy from 70\% to 88\% in a product attribute extraction task. We identify a saturation point of approximately 6,500 samples, beyond which additional data yields diminishing returns. Our proposed bayesian hyperparameter optimization method, which evaluates models at 20\% of total training time, correlates strongly with final model performance, with 4 out of 5 top early-stage models remaining in the top 5 at completion. This approach led to a 2\% improvement in accuracy over baseline models when evaluated on an independent test set. These findings offer actionable insights for practitioners, potentially reducing computational load and dependency on extensive datasets while enhancing overall performance of fine-tuned LLMs.