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:
https://github.com/abilliyb/Knowledge_Injection_Survey_Papers, dedicated to
documenting research in the field of specialized LLM.
67b58e32e972a2806a9a0451
Zirui Song
claimed_verified
2025-02-19T09:00:38.943Z
65407ba7a38390065750233f
https://cdn-avatars.huggingface.co/v1/production/uploads/65407ba7a38390065750233f/1_IPMZbk-S9u2t18PQgMp.jpeg
Zirui Song
user
Ziruibest
67b58e32e972a2806a9a0452
Bin Yan
67b58e32e972a2806a9a0453
Yuhan Liu
claimed_verified
2025-02-21T09:59:53.429Z
627a124ffe55fa0f8ce0eaf7
/avatars/5dd30cf87e60f257ecfa7d2f871d3f33.svg
Serendipity
user
Yuhan
67b58e32e972a2806a9a0454
Miao Fang
67b58e32e972a2806a9a0455
Mingzhe Li
67b58e32e972a2806a9a0456
Rui Yan
67b58e32e972a2806a9a0457
Xiuying Chen
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:
https://github.com/abilliyb/Knowledge_Injection_Survey_Papers, dedicated to
documenting research in the field of specialized LLM.
This survey presents methods to integrate domain-specific knowledge into large language models, categorizing them into dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization, and compares their effectiveness and challenges.
dynamic knowledge injection static knowledge embedding modular adapters prompt optimization large language models natural language understanding text summarization machine translation domain-specific knowledge healthcare chemistry legal analysis specialized tasks datasets benchmarks