Several categories of Large Language Models (LLMs): A Short Survey

TL;DR

This survey summarizes various categories of Large Language Models, highlighting recent developments, methods, datasets, and challenges across different domains like finance, biomedical, vision, and code models.

Contribution

It provides a comprehensive overview of LLM subcategories, their methods, attributes, and unresolved challenges, serving as a useful resource for researchers and developers.

Findings

Summarizes recent advances in task-specific LLMs.

Highlights key datasets and transformer architectures.

Identifies unresolved issues in chatbot and virtual assistant development.

Abstract

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.