Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025)

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: blueofficial-repo.com, dedicated to documenting research in the field of specialized LLM.

## introduction llms have achieved extraordinary success across various tasks, showcasing remarkable capabilities in reasoning, knowledge representation, and decision-making @xcite . however, despite their impressive performance in general-purpose applications, many specialized domains, such as healthcare, chemistry, and legal * equal contributions. despite these advancements, early efforts in knowledge injection often treated domains independently, leading to a lack of standardization in methodologies and evaluation. as the volume of research continues to grow rapidly, with applications and studies proliferating across disciplines, the need for a comprehensive review becomes evident. this review aims to summarize the state of knowledge injection techniques, provide a systematic blueprint for future research, and identify key challenges, such as balancing scalability with domain-specific accuracy and enabling efficient, real-time knowledge updates. we begin in section 2 with background on domain-specific knowledge and its role in llms. section 3 presents a unified framework of four knowledge injection paradigms: (1) dynamic knowledge injection at inference time; (2) static knowledge embedding during training or finetuning; (3) modular adapters for parameterefficient integration; and (4) prompt optimization via carefully designed inputs. section 4 examines these methods across domains such as materials science, chemistry, biology, and law. section 5 summarizes key datasets, tools, and comparative results. section b outlines open challenges, including scalability, robustness, and domain transfer. finally, section 7 concludes the paper and reflects on future directions. ## paradigms of knowledge injection to systematically understand how domain knowledge is integrated into llms, we categorize existing approaches into four paradigms based on when the knowledge is incorporated and how it interacts with the model, as shown in figure 2 . specifically, we utilize unified notations, as described in ## challenges and opportunities integrated knowledge consistency. knowledge injection allows llms to incorporate and integrate different domain-specific knowledge. however, retrieved knowledge may conflict with the model's pre-trained representations or other retrieved facts, leading to inconsistencies in outputs @xcite . for example, in healthcare or legal analysis, conflicting treatment protocols or contradictory legal precedents could arise @xcite , resulting in unreliable decisions and undermining the system's trustworthiness. to address this, future research must focus on detecting inconsistencies, resolving conflicts, and maintaining consistency in integrated knowledge. conflicts can be addressed by prioritizing reliable sources, applying domainspecific rules, or using ensemble techniques to balance multiple perspectives. alignment and validation modules help ensure retrieved knowledge fits the model's reasoning. cross-domain knowledge transfer. crossdomain knowledge transfer involves equipping llms with the ability to generalize knowledge across diverse and distinct fields @xcite . while this significantly expands their applicability, it also introduces challenges due to the complexity and diversity of domain-specific terminologies, ontologies, and reasoning patterns @xcite . for example, transferring knowledge from chemistry to healthcare might require reconciling differing data structures and reasoning frameworks @xcite . overcoming these challenges requires advancements in modular knowledge representation and transfer learning techniques. future efforts could explore hybrid approaches that blend static embeddings with dynamic retrieval, enabling llms to adapt knowledge flexibly across domains without compromising depth. additionally, standardized cross-domain benchmarks can enable consistent evaluation and drive innovation in knowledge transfer methods. we provide more discussions in appendix b. ## conclusion llms enhanced by domain-specific knowledge have shown remarkable potential and garnered in-creasing research interest. this survey systematically reviews llm knowledge injection systems, exploring knowledge representation methods, integration strategies, and mechanisms for preserving model generality. we also summarize applications across biomedicine, chemistry, and computational social science domains. by highlighting standard datasets, benchmarks, challenges, and future opportunities, we aim to provide a valuable resource that inspires exploration of knowledge-enhanced llms for domain-specific challenges.