详解行业专用大模型构建方法与挑战
An overview of domain-specific foundation model: key technologies, applications and challenges
- 系统梳理领域专用大模型的构建架构与核心技术
- 覆盖医疗、金融等多领域应用前景
- 适合想定制行业模型的研究者与工程师
ChatGPT等基于基础模型的产品在自然语言理解方面表现卓越，推动学术界与产业界探索如何将这些模型适配于特定行业和应用场景。这一过程称为领域专用基础模型（FMs）的定制化，旨在克服通用模型难以捕捉领域数据独特模式与需求的局限性。尽管该方向至关重要，但针对领域专用基础模型构建的全面综述仍显不足，而通用模型相关资源则较为丰富。为此，本文提供了一篇及时且详尽的综述，涵盖领域专用基础模型的基本概念、通用架构及关键构建方法。文章还讨论了可受益于专用模型的多个领域，并指出未来面临的挑战。通过本综述，我们希望为来自不同领域的研究者与实践者开发专属基础模型提供有价值的指导与参考。
原文摘要 · Abstract (English)
The impressive performance of ChatGPT and other foundation-model-based products in human language understanding has prompted both academia and industry to explore how these models can be tailored for specific industries and application scenarios. This process, known as the customization of domain-specific foundation models (FMs), addresses the limitations of general-purpose models, which may not fully capture the unique patterns and requirements of domain-specific data. Despite its importance, there is a notable lack of comprehensive overview papers on building domain-specific FMs, while numerous resources exist for general-purpose models. To bridge this gap, this article provides a timely and thorough overview of the methodology for customizing domain-specific FMs. It introduces basic concepts, outlines the general architecture, and surveys key methods for constructing domain-specific models. Furthermore, the article discusses various domains that can benefit from these specialized models and highlights the challenges ahead. Through this overview, we aim to offer valuable guidance and reference for researchers and practitioners from diverse fields to develop their own customized FMs.
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