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Bulletin of Chinese Academy of Sciences (Chinese Version)

Keywords

large-scale models, key core technology breakthroughs, industrial ecosystem, technology governance

Abstract

As a pivotal direction for breakthroughs in key core technologies within the artificial intelligence domain, large-scale models hold strategic significance in securing national scientific and technological sovereignty. This study employs a multidimensional framework encompassing “technological breakthroughs, industrial transformation, and governance policies” to systematically investigate the developmental trajectories and industrialization bottlenecks of large-scale models. At the technological level, while large-scale models exhibit exponential growth in parameter scale and computing power demands, they face critical challenges including the scarcity of high-quality data, insufficient transfer learning capabilities, and reliability-explainability trade-offs. Industrially, these models are reshaping the global industrial chain landscape through a dual-track application pattern integrating general-purpose and specialized models, yet their scalable deployment is constrained by a fragmented ecosystem, talent shortages, and security risks. From a policy perspective, intensified international competition and governance complexities necessitate the establishment of dynamic policy frameworks and global collaborative mechanisms. To address these challenges, this study proposes systemic strategies to strengthen indigenous innovation in computing power and algorithms, deepen industry-academia-research collaboration, and improve risk governance frameworks. These recommendations aim to foster the high-quality development of large-scale models and provide strategic support for industrial upgrading.

First page

1683

Last Page

1692

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

[1] Editorial. From einstein to AI: How 100 years have shaped science. Nature, 2023, 624: 474.

[2] Epstein Z, Hertzmann A. Art and the science of generative AI. Science, 2023, 380: 1110-1111.

[3] Strubell E, Ganesh A, McCallum A. Energy and policy consideration for deep learning in NLP// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy, 2019: 3645-3650.

[4] 温馨, 张超, 郭锐, 等. 推动我国大模型开源创新生态建设的挑战与建议. 中国科学院院刊, 2024, 39(8): 1313-1326. Wen X, Zhang C, Guo R, et al. Challenges and recommendations for building open source innovation ecosystem for large-models in China. Bulletin of Chinese Academy of Sciences, 2024, 39(8): 1313-1326. (in Chinese)

[5] Floridi L, Taddeo M. How AI can be a force for good. Science, 2018, 361: 751-752.

[6] 王耀祖, 李擎, 戴张杰, 等. 大语言模型研究现状与趋势. 工程科学学报, 2024, 46(8): 1411-1425. Wang Y Z, Li Q, Dai Z J, et al. Current status and trends in large language modeling research. Chinese Journal of Engineering, 2024, 46(8): 1411-1425. (in Chinese)

[7] Yenduri G, Ramalingam M, Selvi G C, et al. GPT (Generative Pre-Trained Transformer)—A comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions. IEEE Access, 2023, 12: 54608-54649.

[8] 虞苏妍, 左芳芳, 田盼. 人工智能的创新基石：合成数据. 中国工业和信息化, 2024, (11): 10-14. Yu S Y, Zuo F F, Tian P. Cornerstone of artificial intelligence innovation: Synthetic data. China Industry & Information Technology, 2024, (11): 10-14. (in Chinese)

[9] Ahmed N, Wahed M, Thompson N C. The growing influence of industry in AI research. Science, 2023, 379: 884-886.

[10] Shumailov I, Shumaylov Z, Zhao Y, et al. AI models collapse when trained on recursively generated data. Nature, 2024, 631: 755-759.

[11] 郑晓龙, 李家彤. 人工智能时代的开源与闭源技术模式探讨. 中国科学院院刊, 2025, 40(3): 459-464. Zheng X L, Li J T. Discussion on open-source and closed-source technology models in era of artificial intelligence. Bulletin of Chinese Academy of Sciences, 2025, 40(3): 459-464. (in Chinese)

[12] 杜晓明, 赵晔蕾, 张媛玥, 等. 大模型数据集现状与对电信运营商的启示. 互联网天地, 2023, (9): 53-56. Du X M, Zhao L H, Zhang Y Y, et al. Current situation of large model data sets and implications for telecom operators. Internet World, 2023, (9): 53-56. (in Chinese)

[13] Ma H, Gao Q, Li X, et al. AI development and employment skill structure: A case study of China. Economic Analysis and Policy, 2022, 73: 242-254.

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