  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.
