# A Comprehensive Overview of Large Language Models

**Authors:** Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Nick Barnes, Ajmal Mian  
**arXiv:** [2307.06435](https://arxiv.org/abs/2307.06435)  
**Date:** 2023-07-12  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.06435v1/

## Key points

- Large Language Models (LLMs) have excellent generalization capabilities in natural language processing.
- Numerous models have been developed to outperform baselines and propose new architectures and training strategies.
- Open-source alternatives to GPT-3 have been developed, trained on extensive web-based datasets.
- Instruction fine-tuning is a superior approach that incorporates additional prompts or instructions during the fine-tuning phase to guide LLM outputs.
- Parameter-efficient fine-tuning updates only a small number of parameters while maintaining good performance.
- Existing survey papers lack detailed discussions on individual LLMs, unlike this comprehensive survey paper.
- The comprehensive survey provides an in-depth analysis of individual LLMs, including architecture categorization and basics for readers unfamiliar with them.
- It covers historical developments, training datasets, and other granular aspects overlooked in previous surveys.
- The survey paper fills a gap in existing literature by providing comprehensive analysis and detailed discussions on various aspects of LLMs.
- It serves as a valuable resource for researchers and practitioners interested in understanding and developing advanced LLMs.
- Regular updates are needed as new advancements continue to emerge in this rapidly evolving field.

## Summary

Large Language Models (LLMs) have gained significant attention in the field of natural language processing due to their excellent generalization capabilities. This has led to the development of numerous models that propose various new architectures and training strategies to outperform baselines. The closed-source nature of GPT-3 has also prompted the development of open-source alternatives operating at a similar scale and trained on extensive web-based datasets. One approach that has shown superior performance compared to GPT-3 is instruction fine-tuning, which involves incorporating additional prompts or instructions during the fine-tuning phase to guide the output of LLMs. This allows users to have more control over the outputs and improves performance on various benchmarks. Parameter-efficient fine-tuning is another strategy that updates only a small number of parameters while maintaining good performance, minimizing the impact on the overall model's performance. While there are existing survey papers that provide an overview of augmented techniques and cover architectures, fine-tuning, emergent abilities, and usability of LLMs, they often lack detailed discussions on individual models. In contrast, this comprehensive survey aims to provide an in-depth analysis of individual LLMs by discussing fine-grained details. This paper not only analyzes LLM architectures and their categorization but also provides a thorough understanding of the basics behind LLMs for readers who may not be familiar with them. It delves into historical developments, training datasets, and other granular aspects of LLMs that have been overlooked in previous surveys. Overall, this survey paper fills a gap in existing literature by providing a comprehensive analysis and detailed discussions on various aspects of LLMs. It serves as a valuable resource for researchers and practitioners interested in understanding and developing advanced LLMs. Additionally, it highlights the need for regular updates as new advancements continue to emerge in this rapidly evolving field.