Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey
Zirui Song, Bin Yan, Yuhan Liu, Miao Fang, Mingzhe Li, Rui Yan, Xiuying Chen
2025-02-19
Summary
This paper talks about different ways to make large language models (LLMs) better at handling specific topics or fields, like medicine or law, by adding specialized knowledge to them. It's like teaching a really smart computer to become an expert in a particular subject.
What's the problem?
While LLMs are great at understanding and working with language in general, they often struggle when it comes to specialized fields that require deep, specific knowledge. It's like having a student who's good at general subjects but needs extra help to excel in advanced chemistry or legal studies.
What's the solution?
The researchers looked at four main ways to give LLMs specialized knowledge: dynamically adding information as needed, permanently embedding knowledge into the model, using special add-ons called modular adapters, and optimizing the way questions are asked. They compared these methods, discussing how well each one works for different tasks and situations.
Why it matters?
This matters because it could make AI systems much more useful in specialized fields like healthcare, science, and law. By improving LLMs' ability to understand and work with specific topics, we could create AI assistants that are truly helpful to professionals in these fields. This could lead to faster research, more accurate diagnoses, and better legal advice, potentially improving many aspects of our lives that rely on expert knowledge.
Abstract
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: https://github.com/abilliyb/Knowledge_Injection_Survey_Papers, dedicated to documenting research in the field of specialized LLM.