An efficient strategy for fine-tuning large language models.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Machine Learning

Background:

• Adapting Large Language Models (LLMs) to domain-specific tasks is challenging due to high data and compute requirements.
• Existing fine-tuning methods often necessitate extensive resources, limiting their application in data-scarce environments.

Purpose of the Study:

• To propose an end-to-end strategy for rapidly fine-tuning LLMs for domain-specific tasks under limited data and compute.
• To evaluate the effectiveness of Distilling Step-by-Step (DSS) combined with different fine-tuning modalities.

Main Methods:

• Employed Distilling Step-by-Step (DSS) for dataset creation and model training, utilizing Chain-of-Thought prompting for rationale generation.
• Benchmarked three fine-tuning approaches: full-precision, Low-Rank Adaptation (LoRA), and Quantized LoRA (QLoRA) via hyperparameter sweeps.
• Conducted an ablation study comparing DSS with rationale supervision against label-only supervision.

Main Results:

• DSS with full-precision fine-tuning achieved the highest performance.
• DSS with LoRA offered a strong performance-efficiency tradeoff under resource constraints.
• DSS with QLoRA enabled training within tighter GPU memory budgets while maintaining competitive results.

Conclusions:

• The proposed DSS strategy provides a practical workflow for resource-constrained domain adaptation of LLMs.
• The choice of fine-tuning modality (full-precision, LoRA, or QLoRA) should be based on available computational resources.
• This approach facilitates efficient LLM fine-tuning for domain-specific applications with limited data.