Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning?

TL;DR

This paper explores how continual pre-training with LoRA can improve large language models' ability to learn deeper domain-specific insights in medicine and finance, emphasizing the importance of document modification for better insight extraction.

Contribution

It demonstrates that document modification significantly enhances LLMs' insight learning through continual pre-training with LoRA in specific domains.

Findings

Document modification improves insight learning

Continual pre-training marginally benefits original documents

Enhanced insight extraction in medicine and finance

Abstract

Large Language Models (LLMs) have demonstrated remarkable performance on various tasks, yet their ability to extract and internalize deeper insights from domain-specific datasets remains underexplored. In this study, we investigate how continual pre-training can enhance LLMs' capacity for insight learning across three distinct forms: declarative, statistical, and probabilistic insights. Focusing on two critical domains: medicine and finance, we employ LoRA to train LLMs on two existing datasets. To evaluate each insight type, we create benchmarks to measure how well continual pre-training helps models go beyond surface-level knowledge. We also assess the impact of document modification on capturing insights. The results show that, while continual pre-training on original documents has a marginal effect, modifying documents to retain only essential information significantly enhances the insight-learning capabilities of LLMs.