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

**Authors:** Saurabh Pahune, Manoj Chandrasekharan  
**arXiv:** [2307.10188](https://arxiv.org/abs/2307.10188)  
**Date:** 2023-07-05  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.10188v1/

## Key points

- The essay explores the effectiveness of Large Language Models (LLMs) in natural language processing and their applications in various fields.
- The authors provide a concise summary of different subcategories of LLMs, including task-based financial LLMs, multilingual language LLMs, biomedical and clinical LLMs, vision language LLMs, and code language models.
- The methods used to develop these models as well as their attributes and datasets are summarized for each category.
- Transformer models are utilized and comparison metrics are applied for evaluation purposes.
- Unresolved challenges in developing chatbots and virtual assistants using LLMs are discussed, including enhancing natural language processing capabilities, improving chatbot intelligence, and tackling moral and legal dilemmas associated with these technologies.
- The study serves as a valuable resource for developers, academics, and users seeking to understand the different categories of LLMs and their potential applications.
- It offers information about current advancements in the field while also providing future directions for further research and development.

## Summary

This essay, titled "Several categories of Large Language Models (LLMs): A Short Survey," written by Saurabh Pahune and Manoj Chandrasekharan, explores the effectiveness of Large Language Models (LLMs) in natural language processing and their applications in various fields. The authors provide a concise summary of different subcategories of LLMs, focusing on recent developments and efforts made in each category. The survey highlights several types of LLMs, including task-based financial LLMs, multilingual language LLMs, biomedical and clinical LLMs, vision language LLMs, and code language models. For each category, the authors summarize the methods used to develop these models as well as their attributes and datasets. Additionally, they discuss the transformer models utilized and comparison metrics applied for evaluation purposes. This comprehensive overview allows readers to gain insights into the specific characteristics and capabilities of each type of LLM. Furthermore, the essay sheds light on unresolved challenges in developing chatbots and virtual assistants using LLMs. It addresses issues such as enhancing natural language processing capabilities, improving chatbot intelligence, and tackling moral and legal dilemmas associated with these technologies. By highlighting these problems, the authors aim to provide valuable information for readers interested in LLM-based chatbots and virtual intelligent assistant technologies. Overall,this study serves as a valuable resource for developers, academics,and users seeking to understand the different categories of LLMsand their potential applications.It offers useful information about current advancements in the field while also providing future directions for further researchand development.