大模型技术及其在垂直领域应用综述

A Review of Large Language Model Technology and Applications in Vertical Fields

摘要:

大语言模型是一类由具有大量参数的人工神经网络构成的语言模型，通常通过自监督学习或半监督学习方式，在大量未标注文本上进行训练，是当前生成式人工智能技术的核心组成部分，在机器翻译、问答系统、对话生成等多个任务中表现出色。然而，现有的综述性研究多侧重于LLMs的理论框架与训练方法，对垂直领域的介绍相对不足，且局限于特定领域。为更全面地应对大模型在自然语言处理领域的发展及其对通用任务和行业应用带来的影响，在系统梳理LLMs的基础架构、训练技术与发展脉络的基础上，通过对其在若干具有广阔前景的垂直领域——如科学研究、医疗建康、智慧教育、现代农业等——的应用现状进行系统分析，深入探讨了大模型在实际应用中面临的挑战，提出相应的应对策略，并展望未来的研究方向。

Abstract:

Large Language Models are a type of language model composed of artificial neural networks with a large number of parameters. They are typically trained on a vast amount of unlabeled text through self-supervised or semi-supervised learning methods and are a core component of current generative artificial intelligence technology. They have performed well in multiple tasks such as machine translation, question-answering systems, and dialogue generation. However, existing review studies mostly focus on the theoretical framework and training methods of LLMs, with relatively insufficient coverage of vertical fields and are limited to specific domains. To comprehensively address the development of large models in the field of natural language processing and their impact on general tasks and industry applications, this paper systematically reviews the basic architecture, training techniques, and development trajectory of LLMs. It then conducts a systematic analysis of their application status in several promising vertical fields, such as scientific research, medical health, smart education, and modern agriculture. It delves into the challenges faced by large models in practical applications, proposes corresponding strategies to address these challenges, and looks forward to future research directions.