Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogeneity of domain data, the sophistication of domain knowledge, the uniqueness of domain objectives, and the diversity of the constraints (e.g., various social norms, cultural conformity, religious beliefs, and ethical standards in the domain applications). Domain specification techniques are key to make large language models disruptive in many applications. Specifically, to solve these hurdles, there has been a notable increase in research and practices conducted in recent years on the domain specialization of LLMs. This emerging field of study, with its substantial potential for impact, necessitates a comprehensive and systematic review to better summarize and guide ongoing work in this area. In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications. First, we propose a systematic taxonomy that categorizes the LLM domain-specialization techniques based on the accessibility to LLMs and summarizes the framework for all the subcategories as well as their relations and differences to each other. Second, we present an extensive taxonomy of critical application domains that can benefit dramatically from specialized LLMs, discussing their practical significance and open challenges. Last, we offer our insights into the current research status and future trends in this area.

翻译：大语言模型（LLMs）显著推动了自然语言处理（NLP）领域的发展，为各类应用提供了高度实用且任务无关的基础支撑。然而，将LLMs直接应用于特定领域的复杂问题面临诸多障碍，这些障碍源于领域数据的异质性、领域知识的复杂性、领域目标的独特性，以及约束条件（如领域应用中不同的社会规范、文化趋同、宗教信仰和伦理标准）的多样性。领域专业化技术是实现大语言模型在诸多应用中颠覆性价值的关键。具体而言，为攻克这些难题，近年来围绕LLMs的领域专业化方向涌现了大量研究与实践。这一新兴研究领域虽潜力巨大，却亟需系统性的全面综述以总结并指导现有工作。本文对面向大语言模型的领域专业化技术进行了综合调研——这一新兴方向对大语言模型应用至关重要。首先，我们提出了一套系统分类法，基于LLMs的可访问性对其领域专业化技术进行归类，并总结了各子类别的框架及其相互关系与差异。其次，我们构建了能从专业化LLMs中显著受益的关键应用领域的广泛分类体系，探讨了其实践意义与开放挑战。最后，我们对该领域的研究现状和未来趋势提出了见解。