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        <title>Efficient Continual Pre-training for Building Domain Specific Large Language Models</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Efficient Continual Pre-training for Building Domain Specific Large Language Models
%A Xie, Yong
%A Aggarwal, Karan
%A Ahmad, Aitzaz
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F xie-etal-2024-efficient
%X 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.
%U https://aclanthology.org/2024.findings-acl.606
%P 10184-10201

Markdown (Informal)

[Efficient Continual Pre-training for Building Domain Specific Large Language Models](https://aclanthology.org/2024.findings-acl.606) (Xie et al., Findings 2024)

• Efficient Continual Pre-training for Building Domain Specific Large Language Models (Xie et al., Findings 2024)

ACL

• Yong Xie, Karan Aggarwal, and Aitzaz Ahmad. 2024. Efficient Continual Pre-training for Building Domain Specific Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 10184–10201, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.