Title: How AI and Large Language Models Are Changing Academic Search – An Academic Librarian’s View (人工智能和大语言模型如何改变学术搜索——一位大学图书馆员的视角)
Date (日期): December 12, 2024 (2024年12月12日)
Time (时间): 9:00-10:00 AM Beijing Time (北京时间上午9-10点)
Registration Link (注册链接): https://nyu.zoom.us/meeting/register/tJwofuqopz4iGdBZvBlF_YtEAGNCAIubFH4H#/registration
Language (语言): English (英文)
Speaker: Aaron Tay is an academic librarian with over 15 years of experience in various areas of librarianship. He is currently the Head of Data Services at Singapore Management University Libraries, where he oversees and leads a team of librarians providing research support in discovery, open access, and other emerging areas. Aaron has a keen interest and curiosity in information science and library science, and he shares his thoughts on his award-winning blog Musings about Librarianship. He has also received several honors and awards, including the Karl Lo Award from the Pacific Rim Research Libraries Alliance and the Library Association of Singapore Professional Service Award. He is active on Twitter and recently Bluesky with the account @aarontay
主讲人简介：Aaron Tay，一位超过 15 年从业经验的大学图书馆员，涉猎图书馆学的多个领域。他目前担任新加坡管理大学图书馆数据服务部主任，负责领导一个提供研究支持的馆员团队，包含学术发现、开放获取及其他新兴领域。Aaron 对信息科学和图书馆学有浓厚的兴趣和好奇心，他在自己备受赞誉的博客《Musings about Librarianship》中分享了诸多个人见解。他曾获得多个荣誉和奖项，包括环太平洋研究型图书馆联盟的 Karl Lo 奖和新加坡图书馆协会的专业服务奖。他活跃于 Twitter ，最近加入了 Bluesky，账号为 @aarontay。
Abstract: In the fast-evolving world of academic research, AI tools for literature search and synthesis are gaining prominence. These tools promise to boost productivity, helping researchers achieve more in less time and absorb vast amounts of knowledge efficiently. With the increasing number of these systems, academic librarians must ask: are these tools worth our time and investment? And if so, how do we choose the best ones? Or, even more fundamentally, should we even be encouraging such tools?
In this talk, he will share insights on two popular categories of AI tools: “Citation-based literature mapping tools” like Connected Papers, ResearchRabbit, and Litmaps, and transformer-based large language models (LLMs) such as OpenAI’s GPT models. He will also discuss the main ways in which LLMs are being used in academic search engines like Elicit, SciSpace, and Scite Assistant for both narrative reviews, highlight their limitations, and provide suggestions for librarians to add value.
在快速发展的学术研究领域，AI 工具在文献搜索和综述方面正日益受到关注。这些工具承诺能够提高生产力，帮助研究人员在更短的时间内完成更多工作，并高效地吸收大量知识。随着这类系统的不断增多，大学图书馆员必须思考：这些工具值得我们投入时间和资源吗？如果值得，我们该如何选择最合适的工具？更根本的问题是，我们是否应该鼓励使用这些工具？
在本次讲座中，他将分享关于两类流行 AI 工具的见解：一类是“基于引用的文献映射工具”，如 Connected Papers、ResearchRabbit 和 Litmaps；另一类是基于转换器的大语言模型（LLM），如 OpenAI 的 GPT 模型。他还将讨论学术搜索引擎（如 Elicit、SciSpace 和 Scite Assistant）中使用 LLM 的主要方式，包括叙述性综述，强调其局限性，并为图书馆员提供增值建议。
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