Enhancing Contextual Data Analysis through Retrieval-Augmented Fine-Tuning of Large Language Models

Abstract

This study explores the optimization of large language models (LLMs) for enhanced contextual data analysis and knowledge extraction from unstructured user-generated content, with a comparative analysis of open-source models (e.g., Mistral 7B, LLaMA 2) and proprietary systems (e.g., GPT-4, Gemini). We evaluate their efficiency and accuracy in processing complex datasets, introducing a novel approach that integrates Retrieval-Augmented Generation (RAG) with fine-tuning techniques like Low-Rank Adaptation (LoRA) to reduce model complexity while preserving performance. Empirical results, using metrics such as BERTScore, ROUGE, and BLEU, show GPT-4 achieving an F1 score of 0.683, while Mistral 7B, a standout open-source model, scores 0.632 with a 40% reduction in computational cost and 92% accuracy retention, making it ideal for resource-constrained environments. These findings underscore the importance of tailoring model selection to computational and organizational needs. The research offers actionable insights for deploying AI-driven solutions to streamline data processing and advance machine learning applications, while addressing limitations and future research directions for broader applicability.