# Large Language Models: A Survey

**Authors:** Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, Jianfeng Gao  
**arXiv:** [2402.06196](https://arxiv.org/abs/2402.06196)  
**Date:** 2024-02-09  
**Summary page:** https://www.summarizepaper.com/en/paper/2402.06196v2/

## Key points

- Large Language Models (LLMs) have gained significant attention for their exceptional performance in natural language tasks
- ChatGPT, released in November 2022, contributed to the increased focus on LLMs
- LLMs achieve their capabilities through training billions of parameters on vast text data
- Various prominent LLM families like GPT, LLaMA, and PaLM are emerging
- The paper discusses characteristics, contributions, and limitations of popular LLM families
- Techniques used to build and enhance LLMs are also covered
- Specific datasets for LLM training and evaluation are explored
- Commonly used evaluation metrics for LLMs are reviewed
- Strengths and weaknesses of different LLMs in various contexts are compared
- Open challenges within the field of LLMs are addressed with potential future research directions outlined

## Summary

Large Language Models (LLMs) have garnered significant attention in recent years for their exceptional performance across a wide range of natural language tasks. This is especially true since the release of ChatGPT in November 2022. These models achieve their impressive general-purpose language understanding and generation capabilities through training billions of parameters on vast amounts of text data, following scaling laws as predicted by previous research. The field of LLMs is rapidly evolving with various prominent LLM families such as GPT, LLaMA, and PaLM emerging. In this paper, we delve into the characteristics, contributions, and limitations of these popular LLM families while also providing an overview of the techniques used to build and enhance them. Additionally, we explore datasets specifically curated for LLM training, fine-tuning, and evaluation and review commonly used evaluation metrics for LLMs. Through a comparative analysis of several popular models on representative benchmarks, we shed light on the strengths and weaknesses of different LLMs in various contexts. As we conclude our discussion, we address open challenges within the field of LLMs and outline potential future research directions. The rapid pace at which new findings, models, and techniques are being introduced underscores the need for a comprehensive survey like this one to serve as a valuable resource for students, researchers, and developers navigating the complex landscape of large language models. Moving forward in this paper- Section II provides an overview of state-of-the-art LLMs with a focus on GPT, LLaMA,and PaLM families. - Section III delves into how LLMs are constructed. - Section IV explores practical applications and augmentation strategies for LLMs. - Sections V and VI review datasets and benchmarks used to evaluate LLM performance. - Finally,in Section VII ,we summarize key challenges facing the fieldand suggest future research avenues to further advance large language models.