Learning Beyond the Surface: How Far Can Continual Pre-Training with LoRA Enhance LLMs' Domain-Specific Insight Learning?
This work addresses the challenge of improving LLMs' domain-specific insight extraction for applications in fields like medicine and finance, though it is incremental as it builds on existing methods like LoRA and continual pre-training.
The study tackled the problem of enhancing large language models' ability to learn deeper domain-specific insights through continual pre-training, finding that modifying documents to retain essential information significantly improved insight-learning capabilities, with specific gains in declarative, statistical, and probabilistic insights across medicine and finance domains.
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.