An overview of domain-specific foundation model: key technologies, applications and challenges

2024-09-06

arXiv.org

AI Summary

General-purpose foundation models struggle to adapt to domain-specific patterns and requirements. Method: This study systematically establishes a theoretical and methodological framework for Domain-Specific Foundation Models (DSFMs), proposing the first cross-industry reusable DSFM customization framework that integrates pretraining-finetuning, instruction alignment, domain adaptation, knowledge injection, and efficient parameter updating, accompanied by a multi-dimensional evaluation system. Results: The framework is validated across ten+ domains—including finance, healthcare, and manufacturing—yielding a comprehensive application landscape; it identifies three universal bottlenecks: data scarcity, computational constraints, and regulatory compliance barriers. This work fills a critical gap in DSFM surveys and delivers the first authoritative, academically rigorous methodology guide and practical reference for industry–academia–research collaboration in DSFM development.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

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, 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 foundation models, 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 foundation models. 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 foundation models.

Problem

• Customizing foundation models for specific industries and applications
• Addressing limitations of general-purpose models in domain-specific contexts
• Providing comprehensive guidance on building domain-specific foundation models

Innovation

• Customizing foundation models for specific domains
• Surveying key methods for domain-specific models
• Addressing challenges in specialized model development