Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey

Large Language Models (LLMs) have demonstrated remarkable success in various tasks such as natural language understanding, text summarization, and machine translation. However, their general-purpose nature often limits their effectiveness in domain-specific applications that require specialized knowledge, such as healthcare, chemistry, or legal analysis. To address this, researchers have explored diverse methods to enhance LLMs by integrating domain-specific knowledge. In this survey, we provide a comprehensive overview of these methods, which we categorize into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization. Each approach offers unique mechanisms to equip LLMs with domain expertise, balancing trade-offs between flexibility, scalability, and efficiency. We discuss how these methods enable LLMs to tackle specialized tasks, compare their advantages and disadvantages, evaluate domain-specific LLMs against general LLMs, and highlight the challenges and opportunities in this emerging field. For those interested in delving deeper into this area, we also summarize the commonly used datasets and benchmarks. To keep researchers updated on the latest studies, we maintain an open-source at: blueofficial-repo.com, dedicated to documenting research in the field of specialized LLM.

Citation

Z. Song, B. Yan, Y. Liu, M. Fang, M. Li, R. Yan, X. Chen, "Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey," 2025, pp. 25297-25311.

Source

Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2025

Conference

30th Conference on Empirical Methods in Natural Language Processing, EMNLP 2025

Source

30th Conference on Empirical Methods in Natural Language Processing, EMNLP 2025

Publisher

Association for Computational Linguistics

DOI

10.18653/v1/2025.findings-emnlp.1379

Additional links

https://aclanthology.org/2025.findings-emnlp.1379/