Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges

TL;DR

This survey reviews recent advancements in Large Language Models and Multimodal LLMs, highlighting their architectures, capabilities, benchmarks, and the challenges faced in their development and deployment.

Contribution

It provides a comprehensive overview and comparative analysis of recent LLM and MLLM architectures, including their technical features, strengths, and limitations.

Findings

MLLMs extend LLM capabilities to multiple data modalities.

Recent LLMs achieve state-of-the-art performance on various benchmarks.

Challenges include model complexity, data requirements, and ethical considerations.

Abstract

Large Language Models (LLMs) represent a class of deep learning models adept at understanding natural language and generating coherent responses to various prompts or queries. These models far exceed the complexity of conventional neural networks, often encompassing dozens of neural network layers and containing billions to trillions of parameters. They are typically trained on vast datasets, utilizing architectures based on transformer blocks. Present-day LLMs are multi-functional, capable of performing a range of tasks from text generation and language translation to question answering, as well as code generation and analysis. An advanced subset of these models, known as Multimodal Large Language Models (MLLMs), extends LLM capabilities to process and interpret multiple data modalities, including images, audio, and video. This enhancement empowers MLLMs with capabilities like video editing, image comprehension, and captioning for visual content. This survey provides a comprehensive overview of the recent advancements in LLMs. We begin by tracing the evolution of LLMs and subsequently delve into the advent and nuances of MLLMs. We analyze emerging state-of-the-art MLLMs, exploring their technical features, strengths, and limitations. Additionally, we present a comparative analysis of these models and discuss their challenges, potential limitations, and prospects for future development.