Large Language Models(LLMs)have become effective tools for natural language
processing and have been used in many different fields. This essay offers a
succinct summary of various LLM subcategories. The survey emphasizes recent
developments and efforts made for various LLM kinds, including task-based
financial LLMs, multilingual language LLMs, biomedical and clinical LLMs,
vision language LLMs, and code language models. The survey gives a general
summary of the methods, attributes, datasets, transformer models, and
comparison metrics applied in each category of LLMs. Furthermore, it highlights
unresolved problems in the field of developing chatbots and virtual assistants,
such as boosting natural language processing, enhancing chatbot intelligence,
and resolving moral and legal dilemmas. The purpose of this study is to provide
readers, developers, academics, and users interested in LLM-based chatbots and
virtual intelligent assistant technologies with useful information and future
directions.

这篇研究论文总结了大型语言模型（LLMs）不同子类的最新发展，包括基于任务的金融 LLMs、多语言 LLMs、生物医学和临床 LLMs，以及视觉语言 LLMs 和代码语言模型。它还强调了聊天机器人和虚拟助手开发领域中的未解决问题，如增强自然语言处理、提升聊天机器人智能性以及解决道德和法律困境，旨在为对基于 LLMs 的聊天机器人和虚拟智能助手技术感兴趣的读者、开发者、学者和用户提供有用的信息和未来方向。