综述大模型架构演进，涵盖多模态能力与未来挑战
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges
- 梳理大模型从单模态到多模态的架构发展路径
- 对比主流多模态模型的技术特点与性能表现
- 适合关注大模型趋势的研究者与开发者参考
大型语言模型（LLMs）是一类具备自然语言理解与连贯响应生成能力的深度学习模型，其复杂度远超传统神经网络，通常包含数十层网络结构，参数量达数十亿至数万亿。这些模型基于Transformer架构，在大规模数据集上训练，具备文本生成、翻译、问答、代码生成与分析等多种功能。更先进的多模态大模型（MLLMs）进一步支持图像、音频、视频等多源信息处理，实现视频编辑、图像理解及视觉内容描述等功能。本文全面综述大模型近期进展，追溯其演进历程，深入探讨MLLMs的兴起与技术细节，分析前沿MLLMs的技术特性、优势与局限，并进行模型对比，讨论当前挑战与未来发展方向。
原文摘要 · Abstract (English)
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.
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