北京市人工智能大模型行业应用分析报告

《北京市人工智能大模型行业应用分析报告》是北京市科学《北京市人工智能大模型行业应用分析报告》发布技术委员会、中关村科技园区管理委员会在2024中关村论坛年会-未来人工智能先锋论坛上发布的报告。

报告显示，北京市目前已备案上线大模型51款，全国第一。大模型应用正从边缘场景向核心场景挺进，在产业升级和金融领域的落地应用步伐最快。

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2024年4月27日，《北京市人工智能大模型行业应用分析报告》发布。

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Multimodal Large Language Models: A Survey

Jiayang Wu1, Wensheng Gan1,2∗, Zefeng Chen1, Shicheng Wan3, Philip S. Yu4

1Jinan University, Guangzhou 510632, China

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2Pazhou Lab, Guangzhou 510330, China

3South China University of Technology, Guangzhou 510641, China

4The University of Illinois Chicago, Chicago, USA

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A Survey on Multimodal Large Language Models

Shukang Yin1* , Chaoyou Fu2∗‡† , Sirui Zhao1∗‡, Ke Li2, Xing Sun2, Tong Xu1, Enhong Chen1‡

1School of CST., USTC & State Key Laboratory of Cognitive Intelligence

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2Tencent YouTu Lab

{xjtupanda,sirui}@mail.ustc.edu.cn, {tongxu,cheneh}@ustc.edu.cn

{bradyfu24}@gmail.com, {tristanli,winfredsun}@tencent.com

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A Survey on Multimodal Large Language Models for Autonomous Driving

Can Cui1*, Yunsheng Ma1*, Xu Cao3,6*, Wenqian Ye4,6*, Yang Zhou5, Kaizhao Liang7, Jintai Chen3,

Juanwu Lu1, Zichong Yang1, Kuei-Da Liao8, Tianren Gao7, Erlong Li2, Kun Tang2, Zhipeng Cao2,

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Tong Zhou2, Ao Liu2, Xinrui Yan2, Shuqi Mei2, Jianguo Cao6†, Ziran Wang1†, Chao Zheng2†

1 Purdue University, West Lafayette, IN, USA 47907

2 Tencent T Lab, Beijing, China

3 University of Illinois Urbana-Champaign, Champaign, IL, USA 61801

4 University of Virginia, Charlottesville, VA, USA 22903

5 New York University, New York, NY, USA 11201

6 PediaMed AI, Shenzhen, China

7 SambaNova Systems, Inc, Palo Alto, CA, USA 94303

8 Objective, Inc, San Francisco, CA, USA 94110

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自20世纪50年代图灵测试被提出以来，研究人员一直在探索和开发能够理解并掌握语言的人工智能技术。作为重要的研究方向之一，语言模型得到了学术界的广泛研究，从早期的统计语言模型和神经语言模型开始，发展到基于Transformer的预训练语言模型。

近年来，研究者们发现通过扩大预训练语言模型的参数量和数据量，大语言模型（Large Language Model）能够在效果显著提升的同时，展示出许多小模型不具备的特殊能力（如上下文学习能力、逐步推理能力等）。最近，作为代表性的大语言模型应用ChatGPT展现出了超强的人机对话能力和任务求解能力，对于整个AI研究社区带来了重大影响。

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为此，中国人民大学高瓴人工智能学院教师和学生调研了大语言模型的最新研究进展和主要技术路径，形成本领域的综述文章一篇，引用或介绍了相关论文420余篇，目前以预印版形式上传到arXiv网站，期望能为各位研究人员和工程人员提供一定的技术参考。

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A Survey of Large Language Models

Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, Ji-Rong Wen

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Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.

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