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（LLM）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能力。然而，将LLM直接应用于解决特定领域的复杂问题时，仍面临诸多障碍，其原因包括领域数据的异质性、领域知识的复杂性、领域目标的独特性以及约束条件的多样性（例如领域应用中各种社会规范、文化适应性、宗教信仰和伦理标准的具体要求）。领域专业化技术是使大语言模型在众多应用中具备颠覆性能力的关键。具体而言，为解决上述障碍，近年来针对LLM的领域专业化研究与实践活动显著增加。这一新兴研究领域具有巨大发展潜力，亟需全面系统的综述，以更好总结并指导该领域的持续工作。本文对LLM领域专业化技术进行了全面综述，这一方向对LLM应用至关重要。首先，我们提出一套系统化的分类体系：根据对LLM的访问权限对领域专业化技术进行分类，并总结各子类别的框架及其相互关系与差异。其次，我们构建了能显著受益于专业化LLM的关键应用领域的广泛分类体系，讨论其实践意义与开放挑战。最后，我们对该领域的研究现状与未来趋势提出见解。