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    Innovative Development of AIGC and GLAM: Review of "Shaping the Future: AIGC and GLAM Innovative Development" Cutting-Edge Academic Forum
    LV Ruijuan, ZHANG Jingbei, YAN Dan, CAI Yingchun
    Journal of Library and Information Science in Agriculture    2023, 35 (5): 27-36.   DOI: 10.13998/j.cnki.issn1002-1248.23-0424
    Abstract328)      PDF(pc) (941KB)(503)       Save
    [Purpose/Significance] The new generation of generative artificial intelligence technology and its applications have entered a new stage of development. Models such as ChatGPT are leading the way in model-driven content generation, ushering in a new era and attracting discussions and attention from various sectors. Currently, there is a wealth of research on artificial intelligence from the perspectives of technological advancements, machine language, algorithm models, application scenarios and so on. However, there are relatively fewer articles specifically focusing on the application of AI generated content (AIGC) in GLAM fields, such as galleries, libraries, art museums. AI in the GLAM field is currently undergoing a transition from digitization to intelligentized level. Therefore, this paper explores the multi-scenario innovative applications of AI in the GLAM field with the theme of "Shaping the Future: AIGC and GLAM Innovative Development", which aims to contribute to the innovation research of AI and cultural, development and provide valuable insights in this field. [Method/Process] The structure of this paper consists of three parts: the evolution and current status of AIGC, the exploration of AIGC's applications in various scenarios within the GLAM field, the risks and challenges faced by AIGC and GLAM in terms of innovative development. Firstly the article provides a brief introduction to the development history of artificial intelligence, the concept of AIGC, algorithm models, typical features, and its current development status both domestically and internationally. Secondly, it presents a review and summary of existing application cases of AIGC in the GLAM field, including the use of virtual scenarios, virtual IP idols, ancient book OCR technology, VR/AR experiences in the fields of image analysis, digital humanities research, and new formats of digital cultural industries. The article analyzes how AIGC enriches the content production, innovation, and user interaction modes, as well as resource management models within GLAM. It also highlights the potential significant advantages in specific areas such as digital individuals, digital collections, and digital media. Lastly, the paper addresses the potential risks and challenges that may arise during the process of AIGC and GLAM's innovative development. These challenges encompass technical aspects, algorithmic biases, network information security, ethical and moral considerations, academic research, publishing and legal regulations. [Results/Conclusions] AIGC possesses advantages such as high efficiency, intelligence, and immersive experiences. It not only brings about transformative changes in content within the GLAM field but also breaks free from traditional interactive modes characterized by singularity and flatness in user engagement. By leveraging technologies such as VR/AR, virtual digital characters, and virtual spaces, AIGC enables diverse and three-dimensional interaction models with customs, thereby significantly expanding the applications in the GLAM field. If we appropriately and fully utilize the valuable opportunities presented by AIGC, it can play a positive role in inheriting and promoting the Chinese excellent civilization and cultural heritage. Furthermore, it will also play a crucial role in accelerating the digital transformation and upgrading of the cultural industry.
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    Towards Known Unknowns: GPT Large Language Models Empower Human-Centered Information Retrieval
    SHOU Jianqi
    Journal of Library and Information Science in Agriculture    2023, 35 (5): 16-26.   DOI: 10.13998/j.cnki.issn1002-1248.23-0386
    Abstract277)      PDF(pc) (1547KB)(315)       Save
    [Purpose/Significance] The foundation of public library services lies within information retrieval (IR), an area that has a profound societal impact through activities such as digital resource integration and the advancement of societal equity. Current methodologies focus primarily on classical keyword-based Online Public Access Catalog (OPAC)-like top-down retrieval and large language model (LLM) based point-to-point retrieval. Unfortunately, these approaches individually fail to strike a balance between flexibility and reliability, hindering the evolution towards user-centric IR systems. Consequently, there is an urgent need for an innovative retrieval strategy that fosters a human-centered IR paradigm. [Method/Process] Contrary to the prevalent school of thought that advocates for the complete substitution of classical OPAC-like approach with LLM methods such as GPT, we put forward a groundbreaking proposal that synergizes the merits of both strategies. This proposition represents the inaugural effort of this kind within the scholarly community of public information service. We introduce the adaptive literature retrieval framework (ALRF), an innovative approach grounded in the principles of cognitive science, addressing the critical user challenge in retrieval - the pursuit of known unknown knowledge (KUK). KUK originates from a user's explicit understanding of the desired outcome, without comprehending the associated domain-specific terminology, thereby lacking the necessary entry point for a keyword-based search. ALRF's novel two-stage workflow caters specifically to such situations: (i) users can identify target keywords or keywords at a more abstract level by entering descriptions in natural language, thus implementing a bottom-up strategy; (ii) utilizing these extracted keywords, users can then conduct a top-down search. ALRF accommodates LLMs such as ChatGPT, GPT-4, and ERNIE Bot. The platform's effectiveness in retrieving literature from diverse fields such as science and engineering, biology and medicine, literature and sociology was carefully evaluated. [Results/Conclusions] The ALRF significantly outperforms standard methods, i.e., LLM-based retrieval service and OPAC-like retrieval service, in terms of both flexibility and reliability. This holds true for tasks involving keyword abstraction (i.e., identifying keywords at a higher level of abstraction in the target domain) and property extraction (i.e., locating keywords with specific attributes but at the same abstraction level as the target domain). Consequently, it addresses the pressing need for KUK retrieval, signifying that ALRF has showcased initial potential to cater to the diverse and personalized retrieval requirements of users. This suggests that ALRF could potentially revolutionize public information services by placing humans at the center of its operation. Regrettably, a current hindrance to the wider adoption of ALRF in public IR in China is the pace of development of powerful LLMs by Chinese corporations. We recommend that researchers remain abreast of such advancements to be cognizant of the realistic possibilities and limitations in real-world applications.
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    Digital Intelligence Integration Innovation Development of GLAM Driven by AIGC
    MA Lecun, ZHAN Xini, ZHU Qiyu, SUN Rong, LI Baiyang
    Journal of Library and Information Science in Agriculture    2023, 35 (5): 4-15.   DOI: 10.13998/j.cnki.issn1002-1248.23-0358
    Abstract240)      PDF(pc) (2499KB)(483)       Save
    [Purpose/Significance] Recently artificial intelligence generated content (AIGC) has become an important tool for the digital development of industry in the digital intelligence integration environment. Clarifying the applicability and application scenarios of AIGC-driven innovation for galleries, libraries, archives and museums (GLAMs) as well as exploring GLAM development challenges will go far towards accelerating the transformation of GLAM digital intelligence and promoting GLAM to realize more value. As the previous research is fragmented and most of the related research only focuses on the impacts brought by a specific emerging technology such as ChatGPT or the application scenarios of a certain type of institution, especially libraries, we aim to explore the applicability, application scenarios and emerging challenges of various AIGC technologies to all GLAM institutions to ensure the systematicity of our research. [Method/Process] Based on the study of previous literature, this paper first elaborates on the applicability of AIGC to the development of GLAM digital intelligence integration from five aspects: policy, academia, industry, data, and users. Then, it analyzes the changes of GLAM digital intelligence scenarios driven by AIGC through the perspectives of technology, business, users and organizational management. Finally a discussion of the challenges faced by GLAM and the expectations for its future of digital intelligence integration is provided. In the section of applicability, policy documents are listed to illustrate the overlapping effect, and a review is provided on the development of the Semantic Web to metaverse, Web 3.0, then to AIGC to show the possibility of continuous research from an academic perspective for the GLAM to utilize AIGC. We demonstrate through several examples such as Hunan Museum that AIGC can be effective in the industry as it can create digital content and help develop the IP industry. It is also found that AIGC can help GLAM, which has a large amount of cultural data, to utilize the data, and AIGC can promote user immersive experience by specific user behavior examples. In terms of scenario change, we exemplify that the introduction of AIGC into GLAM as a collection of new technologies will first cause a huge change in technology, so that the diverse information in GLAM can produce more connections and fusion, and realize a large-scale digital twin. Second, it brings changes to the GLAM business such as AI digital human instead of real person doing inquiry service, and virtual pavilion expanding realistic exhibition hall. It also causes changes in user experience, which becomes strongly interactive, immersive and intelligent and it brings various changes to office style, cooperation mode, management mode and user information management. [Results/Conclusions] AIGC can efficiently promote the innovative development of GLAM under multiple application scenarios. In the future, GLAM institutions should actively address challenges including digital ownership protection and development, technology application benefit input-output ratio, and user conversion and evaluation, continuously promote digital talent training, expand digital technology research, and establish a complete AIGC application evaluation system based on existing resources.
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    Future Learning Centers: A Study on Libraries' Role Reorientation, Function Reconstruction, and Practical Innovations
    ZHANG Jingbei, XU Yaping, ZHOU Qiong, CAI Yingchun
    Journal of Library and Information Science in Agriculture    2023, 35 (6): 43-50.   DOI: 10.13998/j.cnki.issn1002-1248.23-0448
    Abstract240)      PDF(pc) (958KB)(101)       Save
    [Purpose/Significance] A future learning center (FLC), a ground-breaking novel education model crafted in response to the burgeoning demands of a rapidly digitized and intelligent society, will undeniably spearhead the new wave of intelligent construction in contemporary university libraries. As this evolution unfolds, libraries urgently need to clarify and solidify their roles in the meticulous construction of future learning centers. Doing so will promote vibrant knowledge exchange, robustly stimulate cross-disciplinary cooperation, and also guide and embolden libraries to proactively seize unprecedented development opportunities and to collaboratively face the emerging challenges ahead. [Method/Process] To comprehend the genesis and potential of the FLC, this research undertakes a meticulous examination of its foundational concepts, overarching objectives, inherent tasks, and structural framework. It delves deep into the libraries' pivotal role in nurturing these centers, especially through the lens of role delineation and functional metamorphosis. Gleaning insights from real-world examples such as the Beijing Institute of Technology Library and the Shanghai University of Finance and Economics Library, this investigation seeks to capture a snapshot of the current operational landscape, along with its associated tribulations and challenges. Through this way, the research hopes to chart a roadmap, offering invaluable reference and actionable guidance for libraries poised to embark on their journey of creating FLCs. [Results/Conclusions] The future learning center encompasses an intelligent learning environment that melds information services with learning and teaching support. Such spaces are poised to emerge as novel grassroots learning organizations underpinning evolution in learning techniques and the genesis of knowledge. Crafting these future learning centers necessitates a systematic approach, calling for collaboration from various stakeholders. Within this context, libraries serve dual roles as both initiators and active contributors. While libraries' involvement in developing future learning centers remains in nascent stages of growth, the core challenge lies in pioneering a human-centric model that harmoniously fuses space, resources, and services to offer students an enriched, tailored, and novel learning journey. As educational paradigms shift, libraries must capitalize on this momentum, drawing from their foundational expertise in intelligent library development, to resonate with the evolving criteria of talent development. Confronted with myriad challenges-ranging from role definition, conceptual alignment, and overcoming educational reform resistance to enhancing professional acumen and navigating spatial constraints-libraries are suggested to strategically assimilate educational assets, reconfigure spatial methodologies, and proffer intelligent solutions. They should aim at fostering students' proficiency in autonomous, participatory, and investigative learning through diverse resources. In essence, libraries should evolve into hubs for information services, focal points for student learning, and pillars for teaching assistance.
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    Review of Deep Learning for Language Modeling
    WANG Sili, ZHANG Ling, YANG Heng, LIU Wei
    Journal of Library and Information Science in Agriculture    2023, 35 (8): 4-18.   DOI: 10.13998/j.cnki.issn1002-1248.23-0251
    Abstract142)      PDF(pc) (1215KB)(227)       Save
    [Purpose/Significance] Deep learning for language modeling is one of the major methods and advanced technologies to enhance language intelligence of machines at present, which has become an indispensable important technical means for automatic processing and analysis of data resources, and intelligent mining of information and knowledge. However, there are still some difficulties in using deep learning for language modeling for technology development and application service in the library and information science (LIS) field. Therefore, this study systematically reviews and reveals the research progress, technical principles, and development methods of deep learning for language modeling, with the aim at providing reliable theoretical basis and feasible methodological paths for the deep understanding and application of deep learning for language modeling for librarians and fellow practitioners. [Method/Process] The data used in this study were collected from the WOS core database, CNKI literature database, arXiv preprint repository, GitHub open-source software hosting platform and the open resources on the Internet. Based on these data, this paper first systematically investigates the background, basic feature representation algorithms, and representative application development tools of deep learning for language modeling, reveals their dynamic evolution and technical principles, and analyzes the advantages and disadvantages and applicability of each algorithm model and development tool. Second, an in-depth analysis of the possible challenging problems faced by the development and application of deep learning for language modeling was performed, and two strategic approaches to expand their application capabilities were put forward. [Results/Conclusions] The important challenges faced by the application and development of deep learning for language modeling include numerous parameters and difficulties to adjust accuracy, relying on a large amount of accurate training data, difficulties in making changes, and the intellectual property and information security issues. In the future, we will start from two aspects of specific domains and feature engineering to expand and improve the application capabilities of deep learning for language modeling. Specifically, we focus on consideration of the collection and preparation of domain data, selection of model architecture, participation of domain experts, and optimization for specific tasks, in order to ensure that the data source of the model is more reliable and secure, and the application effect is more accurate and practical. Moreover, the strategic methods for feature engineering to expand the application capabilities of deep learning for language modeling include selecting appropriate features, feature pre-processing, feature selection, and feature dimensionality reduction. These strategies can help improve the performance and efficiency of deep learning for language models, making them more suitable for specific tasks or domains. To sum up, LIS institutions should leverage the deep learning for language modeling related technologies, guided by the needs of scientific research and social development, and based on advantages of existing literature data resources and knowledge services; they should carry out innovative professional or vertical domain intelligent knowledge management and application service, and develop technology and systems with independent intellectual property rights, which is their long-term sustainable development path.
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    Research Advances in Argument Mining
    LI Jiao, ZHAO Ruixue, XIAN Guojian, HUANG Yongwen, SUN Tan
    Journal of Library and Information Science in Agriculture    2023, 35 (6): 16-28.   DOI: 10.13998/j.cnki.issn1002-1248.23-0347
    Abstract139)      PDF(pc) (1597KB)(124)       Save
    [Purpose/Significance] Argument mining, a research hotspot in the field of computational linguistics, provides machine processable structured data for computational models of argument. Argument mining tasks are closely related to artificial intelligence (AI) technologies, such as natural language processing and knowledge representation. There are numerous systematic studies in academia and a clear technical realization route has come into being. New research results continue to emerge as a result of rich resources and rapid development and iteration of deep learning, large language models (LLMs), and other technologies. This study, which reviews the research status and progress of argument mining, can serve as a resource for future research and application development. [Method/Process] Through literature review, this paper systematically reviews the relevant research basis (including foundational techniques and semantic representation models), summarizes the related technical system in terms of task framework, influencing factors of technological complexity, and method classification, and then introduces the argument mining practice and application cases for specific fields and research objectives and makes a comparative analysis. Most importantly, the overall development and char