title: Injecting Domain-Specific Knowledge into Large Language Models: A
    Comprehensive Survey
summary: 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.
paper_authors._id: 67b58e32e972a2806a9a0451
paper_authors.name: Zirui Song
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-19T09:00:38.943Z
paper_authors._id: 65407ba7a38390065750233f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65407ba7a38390065750233f/1_IPMZbk-S9u2t18PQgMp.jpeg
paper_authors.fullname: Zirui Song
paper_authors.type: user
paper_authors.user: Ziruibest
paper_authors._id: 67b58e32e972a2806a9a0452
paper_authors.name: Bin Yan
paper_authors._id: 67b58e32e972a2806a9a0453
paper_authors.name: Yuhan Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-21T09:59:53.429Z
paper_authors._id: 627a124ffe55fa0f8ce0eaf7
paper_authors.avatarUrl: /avatars/5dd30cf87e60f257ecfa7d2f871d3f33.svg
paper_authors.fullname: Serendipity
paper_authors.type: user
paper_authors.user: Yuhan
paper_authors._id: 67b58e32e972a2806a9a0454
paper_authors.name: Miao Fang
paper_authors._id: 67b58e32e972a2806a9a0455
paper_authors.name: Mingzhe Li
paper_authors._id: 67b58e32e972a2806a9a0456
paper_authors.name: Rui Yan
paper_authors._id: 67b58e32e972a2806a9a0457
paper_authors.name: Xiuying Chen
paper_title: Injecting Domain-Specific Knowledge into Large Language Models: A
    Comprehensive Survey
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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