A Comprehensive Overview of Large Language Models

TL;DR

This paper offers a comprehensive overview of recent developments in Large Language Models, covering architectural, training, and application advancements to help researchers grasp the field's current landscape.

Contribution

It provides a systematic survey and a quick reference of diverse LLM topics, consolidating recent research to facilitate understanding and future progress.

Findings

Summarizes key advancements in LLM architectures and training strategies.

Highlights recent progress in multi-modal LLMs and efficiency improvements.

Provides a structured overview to aid researchers and practitioners.

Abstract

Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multi-modal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field. This article provides an overview of the existing literature on a broad range of LLM-related concepts. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of research in LLMs. This review article is intended to not only provide a systematic survey but also a quick comprehensive reference for the researchers and practitioners to draw insights from extensive informative summaries of the existing works to advance the LLM research.