将领域特定知识注入大型语言模型：一项全面综述

摘要

大型语言模型（LLMs）在自然语言理解、文本摘要和机器翻译等多种任务中展现了显著的成功。然而，其通用性往往限制了它们在需要专业知识的特定领域应用中的效能，如医疗、化学或法律分析。为解决这一问题，研究者们探索了多种方法，通过整合领域特定知识来增强LLMs。本综述全面概述了这些方法，并将其归纳为四大关键策略：动态知识注入、静态知识嵌入、模块化适配器及提示优化。每种策略均提供了独特的机制，使LLMs具备领域专长，同时平衡了灵活性、可扩展性与效率之间的权衡。我们探讨了这些方法如何助力LLMs应对专业任务，比较了它们的优缺点，评估了领域特定LLMs与通用LLMs的表现，并指出了这一新兴领域面临的挑战与机遇。对于有意深入此领域的研究者，我们还总结了常用的数据集与基准测试。为保持研究者对最新研究的了解，我们维护了一个开源项目，地址为：https://github.com/abilliyb/Knowledge_Injection_Survey_Papers，致力于记录专业LLM领域的研究进展。

English

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.

将领域特定知识注入大型语言模型：一项全面综述

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

摘要

Summary

Support