將領域特定知識注入大型語言模型：一項全面調查

摘要

大型語言模型（LLMs）在多種任務中展現了顯著的成功，例如自然語言理解、文本摘要及機器翻譯。然而，其通用性質往往限制了它們在需要專業知識的特定領域應用中的效能，如醫療保健、化學或法律分析。為解決這一問題，研究者們探索了多種方法，通過整合領域特定知識來增強LLMs。在本調查中，我們全面概述了這些方法，將其分為四大關鍵策略：動態知識注入、靜態知識嵌入、模組化適配器及提示優化。每種策略均提供了獨特的機制，使LLMs具備領域專業知識，同時在靈活性、可擴展性與效率之間取得平衡。我們探討了這些方法如何使LLMs能夠處理專業任務，比較了它們的優缺點，評估了領域特定LLMs與通用LLMs的表現，並強調了這一新興領域的挑戰與機遇。對於有意深入此領域的讀者，我們還總結了常用的數據集與基準測試。為了讓研究者們及時了解最新研究，我們在以下網址維護了一個開源專案：https://github.com/abilliyb/Knowledge_Injection_Survey_Papers，致力於記錄專業LLM領域的研究進展。

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.

將領域特定知識注入大型語言模型：一項全面調查

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

摘要

Summary

Support

Support