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，为广泛的应用提供了高度通用且与任务无关的基础。然而，将LLMs直接应用于解决特定领域的复杂问题面临着诸多挑战，这些挑战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的多样性（例如领域应用中的各种社会规范、文化一致性、宗教信仰和伦理标准）。领域专业化技术是使大语言模型在许多应用中产生颠覆性影响的关键。具体而言，为应对这些挑战，近年来围绕LLMs的领域专业化开展了显著增加的研究与实践。这一新兴研究领域具有巨大的影响潜力，亟需进行全面而系统的综述，以更好地总结并指导该领域的持续工作。在本文中，我们针对大语言模型的领域专业化技术（这一对LLM应用至关重要的新兴方向）进行了全面调研。首先，我们提出了一套系统的分类体系，基于对LLMs的可访问性对领域专业化技术进行分类，并总结了所有子类别的框架及其相互关系与差异。其次，我们针对能够从专业化LLMs中显著受益的关键应用领域进行了广泛分类，讨论了它们的实际意义与开放挑战。最后，我们对该领域的研究现状与未来趋势提出了见解。