Unveiling the Power of Large Language Models: A Comparative Study of Retrieval-Augmented Generation, Fine-Tuning, and Their Synergistic Fusion for Enhanced Performance
Gülsüm Budakoglu, Hakan Emekci
Abstract
Large-language model optimization for a particular application is crucial and challenging in natural language processing. This study compares two salient techniques for retrieve-augmented generation (RAG) and fine-tuning along with a new hybrid method that combines both. In this study, we investigate the effectiveness of various methods using the Stanford Question Answering Dataset (SQuAD), Microsoft Machine Reading Comprehension (MS MARCO) and SQL CREATE TABLE statements. RAG is used because it enriches the model responses with external data without much computational load during the inference. Fine-tuning updates the model parameters to improve the contextual accuracy. Our hybrid model balances the accuracy and efficiency of the two techniques. While fine-tuning entails semantic precision, RAG is more resource efficient. The hybrid approach while it may not offer surpassing results over fine-tuning-offers a balanced solution in scenarios where the application demands both efficiency and accuracy. These findings represent the trade-off involved in LLM optimization and offers a scope for further studies and practical applications.