Efficient Continual Pre-training for Building Domain Specific Large Language Models
This work addresses the challenge of efficiently creating domain-specific LLMs for applications like finance, though it appears incremental as it builds on existing continual pre-training methods.
The paper tackles the problem of building domain-specific large language models by proposing continual pre-training as a cost-effective alternative to full domain-specific training, introducing FinPythia-6.9B which shows consistent improvements on financial tasks and data selection strategies that achieve better performance with 10% of corpus size and cost.
Large language models (LLMs) have demonstrated remarkable open-domain capabilities. LLMs tailored for a domain are typically trained entirely on domain corpus to excel at handling domain-specific tasks. In this work, we explore an alternative strategy of continual pre-training as a means to develop domain-specific LLMs over an existing open-domain LLM. We introduce FinPythia-6.9B, developed through domain-adaptive continual pre-training on the financial domain. Continual pre-trained FinPythia showcases consistent improvements on financial tasks over the original foundational model. We further explore simple but effective data selection strategies for continual pre-training. Our data selection strategies outperform vanilla continual pre-training's performance with just 10% of corpus size and cost, without any degradation on open-domain standard tasks. Our work proposes an alternative solution to building domain-specific LLMs cost-effectively.