中文    English

Journal of library and information science in agriculture ›› 2024, Vol. 36 ›› Issue (12): 20-34.doi: 10.13998/j.cnki.issn1002-1248.24-0666

Previous Articles     Next Articles

DIS Agent: New Paradigm of S&T Documentation and Information Service for the Fifteenth Five-Year Plan

LIU Xiwen1,2, FU Yun1, WEI Huanan1,2   

  1. 1.National Science Library, Chinese Academy of Sciences, Beijing 100190
    2.Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100049
  • Received:2024-10-19 Online:2024-12-05 Published:2025-04-09

RichHTML

13

PDF (PC)

260

Abstract:

[Purpose/Significance] Every transformation and development in scientific and technological (S&T) documentation and information services has revolved around the application of advanced information technologies. Currently, cutting-edge AI technologies such as large-scale models and agents are driving a new wave of paradigm shifts in scientific research. Information institutions should consider how the paradigm of S&T documentation and information services should evolve to lay a strategic foundation for the development of the "15th Five-Year Plan" development. [Method/Process] This study uses objective induction and theoretical reasoning methods. It starts with the three driving modes of AI empowering scientific research and combines them with the essence of information work. The study concludes and summarizes that AI empowers S&T documentation and information services in two main areas: information infrastructure (data production, information organization, and knowledge representation) and information generation (intelligence computation). Agents integrated with large-scale modelling technologies demonstrate exceptional, even scientist-level, data understanding capabilities, suggesting that they are already capable of enabling information generation. [Results/Conclusions] Building and deploying DIS agents is an inevitable choice for information institutions as they prepare for the "15th Five-Year Plan". Driven by DIS agents, S&T documentation and information services will achieve higher levels of automation and intelligence, freeing information professionals from tedious basic data processing tasks and allowing them to focus on generating high-value information and supporting decision making. In the ecosystem of S&T documentation and information services driven by DIS agents, clusters of agents form the core and work together both internally and externally: Internally, DIS agents achieve a high level of automation in four core functions: data production, information organization, knowledge representation, and intelligence computation through the integration of planning tools, basic data and infrastructure resources. Externally, through interactions between agents, information experts, and specific intelligence scenarios, a new working paradigm emerges: "human and multi-agent collaboration". In the future, when planning and designing the implementation of DIS agents, it is essential to focus on both the technical adaptability at the current R&D stage and the potential security risks in future application stages. This ensures the efficient and secure use of DIS agents in S&T documentation and information services.

Key words: 15th Five-Year Plan, documentation and information service agent (DIS agent), human-DIS agent collaboration, S&T documentation and information services, new paradigm

CLC Number: 

  • G350

Table 1

The development history of China S&T documentation and information service"

发展阶段

1956—1978年

第一阶段

1979—1991年

第二阶段

1992—2014年

第三阶段

2015年至今

第四阶段

目标定位

获取国外科技资料

支撑中国战略规划

检索作为核心问题

建立联机检索体系

建设数字图书馆

知识服务支撑决策

建设数据资源体系

情报工作智库化转型

发展特点

建立科技情报机构

形成全国情报系统

电子计算机规范应用

科技情报工作现代化

接入世界互联网络

文献知识广泛流动

数据要素驱动

AI技术应用

Fig.1

The history of CAS’s S&T documentation and information service"

Fig.2

Demand-driven and technology-driven CAS's development of S&T documentation and information service"

Fig.3

Three driving modes of AI driving scientific research"

Fig.4

DIKI and types of information"

Fig.5

Paradigm shift in S&T documentation and information service"

Fig.6

A new ecosystem of S&T documentation and information services based on DIS agents"

1 MILOJEVIĆ S, SUGIMOTO C R, YAN E J, et al. The cognitive structure of library and information science: Analysis of article title words[J]. Journal of the American society for information science and technology, 2011, 62(10): 1933-1953.
2 OPENAI. Hello GPT-4o[EB/OL]. [2024-05-13]. .
3 TOUVRON H, LAVRIL T, IZACARD G, et al. LLaMA: Open and efficient foundation language models[J/OL]. arXiv preprint arXiv:, 2023.
4 GUO T C, CHEN X Y, WANG Y Q, et al. Large language model based multi-agents: A survey of progress and challenges[J/OL]. arXiv preprint arXiv:, 2024.
5 MESSERI L, CROCKETT M J. Artificial intelligence and illusions of understanding in scientific research[J]. Nature, 2024, 627(8002): 49-58.
6 YANG J, LI A, FARAJTABAR M, et al. Learning to incentivize other learning agents[J/OL]. arXiv preprint ArXiv: , 2020.
7 SUN W, YAN L, MA X, et al. Is ChatGPT Good at Search? Investigating Large language models as re-ranking agent[J/OL]. arXiv preprint ArXiv: , 2023.
8 OUYANG S Q, LI L. AutoPlan: Automatic planning of interactive decision-making tasks with large language models[C]//Findings of the Association for Computational Linguistics: EMNLP 2023. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023.
9 XUE S Q, ZHOU F, XU Y, et al. WeaverBird: Empowering financial decision-making with large language model, knowledge base, and search engine[J/OL]. arXiv preprint ArXiv: , 2023.
10 GUAN Y, WANG D, CHU Z, et al. Intelligent virtual assistants with LLM-based process automation[J/OL]. arXiv preprint ArXiv: , 2023.
11 YAO S, YU D, ZHAO J, et al. Tree of thoughts: Deliberate problem solving with large language models[J/OL]. arXiv preprint ArXiv: , 2023.
12 SHINN N, CASSANO F, LABASH B, et al. Reflexion: Language agents with verbal reinforcement learning[C]// Proceedings of the Neural Information Processing Systems, New Orleans, USA: the NIPS Foundation, 2023.
13 LI M H, ZHAO Y X, YU B W, et al. API-bank: A comprehensive benchmark for tool-augmented LLMs[C]//Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023.
14 CHU Z, WANG Y, ZHU F, et al. Professional agents - Evolving large language models into autonomous experts with human-level competencies[J/OL]. arXiv preprint ArXiv: , 2024.
15 李国杰. 智能化科研(AI4R): 第五科研范式[J]. 中国科学院院刊, 2024, 39(1): 1-9.
LI G J. AI4R: The fifth scientific research paradigm[J]. Bulletin of Chinese academy of sciences, 2024, 39(1): 1-9.
16 TSHITOYAN V, DAGDELEN J, WESTON L, et al. Unsupervised word embeddings capture latent knowledge from materials science literature[J]. Nature, 2019, 571(7763): 95-98.
17 刘细文. 贯彻落实二十大精神, 开创文献情报工作的高质量发展道路[J]. 图书情报工作, 2023, 67(1): 4-8.
LIU X W. Implementing the spirit of the 20th national congress of CPC and planning the road of high-quality development of library and information service[J]. Library and information service, 2023, 67(1): 4-8.
18 刘细文, 付芸. 数智赋能背景下情报学研究进展[J]. 情报学进展, 2024, 15: 131-71.
LIU X W, FU Y. The research advancements in information science amidst data & AI empowerment: Data-Driven, model-driven and knowledge discovery[J]. Advances in information science, 2024, 15: 131-71.
19 刘细文, 孙蒙鸽, 王茜, 等. DIKIW逻辑链下GPT大模型对文献情报工作的潜在影响分析[J]. 图书情报工作, 2023, 67(21): 3-12.
LIU X W, SUN M G, WANG X, et al. Analysis of the potential impact of GPT large model under DIKIW logic chain on documentation and information services[J]. Library and information service, 2023, 67(21): 3-12.
20 孙蒙鸽, 韩涛, 王燕鹏, 等. GPT技术变革对基础科学研究的影响分析[J]. 中国科学院院刊, 2023, 38(8): 1212-1224.
SUN M G, HAN T, WANG Y P, et al. Impact analysis of GPT technology revolution on fundamental scientific research[J]. Bulletin of Chinese academy of sciences, 2023, 38(8): 1212-1224.
21 刘细文. 情报学范式变革与数据驱动型情报工作发展趋势[J]. 图书情报工作, 2021, 65(1): 4-11.
LIU X W. Paradigm transformation of library and information science and trends of data-driven information services[J]. Library and information service, 2021, 65(1): 4-11.
22 符福峘. 我国情报学理论体系建设的伟大成就——庆祝我国科技情报事业创建50周年[J]. 情报理论与实践, 2006, 29(4): 385-389.
FU F H. The great achievement of China in the construction of the theoretical system of information science[J]. Information studies: Theory & application, 2006, 29(4): 385-389.
23 关家麟, 张超. 我国科技信息事业发展的回顾与展望[J]. 情报科学, 2007, 25(1): 1-7.
GUAN J L, ZHANG C. The review and outlook for scientific and technological information undertaking of China[J]. Information science, 2007, 25(1): 1-7.
24 包昌火, 王秀玲, 李艳. 中国情报研究发展纪实[J]. 情报理论与实践, 2010, 33(1): 1-3.
BAO C H, WANG X L, LI Y. A record of the development of intelligence analysis in China[J]. Information studies: Theory & application, 2010, 33(1): 1-3.
25 中共中央文献研究室. 建国以来重要文献选编-第九册[M]. 北京: 中央文献出版社, 1994.
26 刘如, 吴晨生, 刘彦君, 等. 中国科技情报工作的传承与发展[J]. 情报学报, 2019, 38(1): 38-45.
LIU R, WU C S, LIU Y J, et al. History and development of scientific and technical intelligence in China[J]. Journal of the China society for scientific and technical information, 2019, 38(1): 38-45.
27 钱学森. 情报资料、图书、文献和档案工作的现代化及其影响[J]. 档案学通讯, 1979(5): 6-10.
QIAN X S. Modernization of information materials, books, documentation and archives and their implications[J]. Archives science bulletin, 1979(5): 6-10.
28 霍国庆, 汪冰. 穿越冷战的情报科学史及其启示: 理查兹“情报科学与冷战的结束”评介[J]. 情报科学, 1998, 16(2): 89-95.
HUO G Q, WANG B. The history of information technology across the cold war and its revelation - The review of Richard's information science and the end of the cold war[J]. Information science, 1998, 16(2): 89-95.
29 刘细文. 中国科学院文献情报中心“十四五”发展思考——基于数据、信息、知识与情报的规划框架设计[J]. 数字图书馆论坛, 2021(5): 12-16.
LIU X W. On the 14th Five-Year Plan of national science library of Chinese academy of sciences: Frameworks design based on the notion of data-information-knowledge-intelligence[J]. Digital library forum, 2021(5): 12-16.
30 孟广均. 中国科学院文献情报中心近40年发展概况(1978-2018)[J]. 图书馆理论与实践, 2019(8): 18-19, 31.
MENG G J. An overview of the development of the Chinese academy of sciences in recent 40 years(1978-2018)[J]. Library theory and practice, 2019(8): 18-19, 31.
31 刘细文. 对中国科学院文献情报中心业务改革与创新的初步认识[J]. 图书情报工作, 2002, 46(6): 11.
LIU X W. A preliminary understanding of the business reform and innovation of the document and information center of Chinese Academy of Sciences[J]. Library and information service, 2002, 46(6): 11.
32 张晓林. 超越图书馆: 寻求变革方向——第77届国际图联大会观感[J]. 图书情报工作, 2011, 55(21): 5-10.
ZHANG X L. Libraries beyond libraries: Seeking for transformative directions - Notes from IFLA 2011[J]. Library and information service, 2011, 55(21): 5-10.
33 谭晓, 靳晓宏. 数智时代情报流程模型构建研究[J]. 情报理论与实践, 2023, 46(10): 32-39.
TAN X, JIN X H. Research on the construction of intelligence process model in the digital intelligence era[J]. Information studies: Theory & application, 2023, 46(10): 32-39.
34 周晓英, 陈燕方, 张璐. 中国科技情报事业发展历程与发展规律研究[J]. 科技情报研究, 2019, 1(1): 13-28.
ZHOU X Y, CHEN Y F, ZHANG L. Research on the development process and law of scientific and technical information career in China[J]. Scientific information research, 2019, 1(1): 13-28.
35 吴晨生, 李辉, 付宏, 等. 情报服务迈向3.0时代[J]. 情报理论与实践, 2015, 38(9): 1-7.
WU C S, LI H, FU H, et al. Intelligence service towards the era of 3.0[J]. Information studies: Theory & application, 2015, 38(9): 1-7.
36 霍忠文, 阎旭军. “情报”、“Informagence”与“Infotelligence”——科技情报工作科学技术属性再思考[J]. 情报理论与实践, 2002, 25(1): 1-5.
HUO Z W, YAN X J. "Information", "informagence" & "infotelligence"[J]. Information studies: Theory & application, 2002, 25(1): 1-5.
37 KOJIMA T, GU S S, REID M, et al. Large language models are zero-shot reasoners[J]. Advances in neural information processing systems, 2022, 35: 22199-22213.
38 张晓林. Inside-Out & Outside-In: 图书馆服务社会高质量发展的组合模型[J]. 图书馆杂志, 2024, 43(9): 4-10.
ZHANG X L. Inside-out & outside-In: A combinatorial model for libraries' support of the high-quality development of the society[J]. Library journal, 2024, 43(9): 4-10.
39 李荣, 吴晨生, 董洁, 等. ChatGPT对开源情报工作的影响及对策[J]. 情报理论与实践, 2023, 46(5): 1-5.
LI R, WU C S, DONG J, et al. Study on the impact of ChatGPT on open source intelligence work and countermeasures[J]. Information studies: Theory & application, 2023, 46(5): 1-5.
40 GHAFAROLLAHI A, BUEHLER M J. SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning[J/OL]. arXiv preprint ArXiv: , 2024.
41 WANG H C, FU T F, DU Y Q, et al. Scientific discovery in the age of artificial intelligence[J]. Nature, 2023, 620(7972): 47-60.
42 SUH C, FARE C, WARREN J A, et al. Evolving the materials genome: How machine learning is fueling the next generation of materials discovery[J]. Annual review of materials research, 2020, 50: 1-25.
43 JUMPER J, EVANS R, PRITZEL A, et al. Highly accurate protein structure prediction with AlphaFold[J]. Nature, 2021, 596(7873): 583-589.
44 KITANO H. Nobel Turing Challenge: Creating the engine for scientific discovery[J]. NPJ systems biology and applications, 2021, 7(1): 29.
45 SUFI F. Generative pre-trained transformer (GPT) in research: A systematic review on data augmentation[J]. Information, 2024, 15(2): 99.
46 ZHANG Y, GE F, LI F Y, et al. Prediction of multiple types of RNA modifications via biological language model[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2023, 20(5): 3205-3214.
47 ZHOU G, GAO Z, DING Q, et al. Uni-Mol: A universal 3D molecular representation learning framework[C]//Proceedings of the International Conference on Learning Representations, Kigali, Rwanda: The International Conference on Learning Representations, 2023.
48 NIJKAMP E, RUFFOLO J A, WEINSTEIN E N, et al. ProGen2: Exploring the boundaries of protein language models[J]. Cell systems, 2023, 14(11): 968-978.e3.
49 SANTACROCE M, LU Y, YU H, et al. Efficient RLHF: Reducing the memory usage of PPO[J/OL]. arXiv preprint ArXiv: , 2023.
50 LEE H, PHATALE S, MANSOOR H, et al. RLAIF vs. RLHF: Scaling reinforcement learning from human feedback with AI feedback[C]//Proceedings of the International Conference on Machine Learning, Honolulu, Hawaii, USA: International Machine Learning Society (IMLS), 2023.
51 钱学森. 科技情报工作的科学技术[J]. 兵工情报工作(1987年改名《情报理论与实践》), 1983, 6(6): 3-10.
QIAN X S. Science and technology of scientific and technological information work[J]. Information studies: Theory & application, 1983, 6(6): 3-10.
52 HIXSON W L. The wizards of langley: Inside the CIA's directorate of science and technology[J]. Journal of American history, 2002, 89(3): 1115-1116.
53 SHERMAN K. Strategic intelligence for American world policy[M]. Princeton, N.J.: Princeton University Press, 1966.
54 LIEW A. DIKIW: Data, information, knowledge, intelligence, wisdom and their interrelationships[J]. Business management dynamics, 2013, 2(10): 49-62.
55 Innovation management - Tools and methods for strategic intelligence management - Guidance: ISO 56006: 2021[S]. International Organization for Standardization [iso], 2021.
56 EVANS J, RZHETSKY A. Machine science[J]. Science, 2010, 329(5990): 399-400.
57 钱力, 刘细文, 张智雄, 等. 科技情报智慧数据: 方法、体系与应用[J]. 情报理论与实践, 2024, 47(1): 12-21.
QIAN L, LIU X W, ZHANG Z X, Et al. Smart data for scientific and technological information: Method, framework and application[J]. Information studies: Theory & application, 2024, 47(1): 12-21.
58 KING R, ZENIL H. A framework for evaluating the AI-driven automation of science[M]. Paris: OECD Publishing, 2023.
59 HUANG Y. Levels of AI agents: From rules to large language models[J/OL]. arXiv preprint ArXiv: , 2024.
60 LU C, LU C, LANGE R T, et al. The AI scientist: Towards fully automated open-ended scientific discovery[J/OL]. arXiv preprint ArXiv: 2408.06292, 2024
61 Crowdflower. Data science report[R]. 2016. San Francisco, USA: Crowdflower. https://www2.cs.uh.edu/~ceick/UDM/CFDS16.pdf.
62 XI Z H, CHEN W X, GUO X, et al. The rise and potential of large language model based agents: A survey[J/OL]. arXiv preprint ArXiv: , 2023.
[1] Xinya YANG, Weiran RAN, Li LI. Key Points of the 15th Five-Year Plan for University Libraries to Support the Construction of a Powerful Country in the Education Sector [J]. Journal of library and information science in agriculture, 2024, 36(12): 35-44.
[2] KE Ping, LI Xiaoying, SUN Xuan, LIU Yue. Drafting the "15th Five-Year Plan" of Chinese Libraries: Constructing the Strategic Goals and Systems Based on Environmental Scanning and Demand Analysis [J]. Journal of library and information science in agriculture, 2024, 36(12): 4-19.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Editorial Board of Journal of library and information science in agriculture
Tel：(010)82109667　Fax：(010)82109667　E-mail：xuekan＠caas.cn
Powered by Beijing Magtech Co., Ltd.
知识共享许可协议