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Location
Singapore
Start Date
28-5-2024 9:15 AM
End Date
28-5-2024 10:45 AM
Description
In the ever-evolving landscape of academic research, “AI tools” for literature search and synthesis are currently getting a lot of attention. These tools promise to ramp up productivity, enabling us to accomplish more in less time or absorb more knowledge without drowning in endless reading. With the sheer number of these systems increasing daily, it's natural to wonder: are they really worth our time and money? And if they are, how should we go about picking the right one from the multitude of options?
In this talk, I will share my views on how the space has developed over two increasingly popular but distinct set of what many call “AI tools”. The first is what I have dubbed “Citation based literature mapping tools”, which includes tools like Connected Papers, ResearchRabbit and Litmaps.
Secondly, the rise of transformer based large language (LLMs) models like OpenAI’s GPT models has also great implications on how academic discovery might change and in this talk I will highlight the 3 main ways in which the Natural language understanding and natural language understanding capabilities of LLMs are being used in currenct academic search engines such as Elicit, SciSpace and Scite assistant and the limits of such tools.
Document Type
Presentation
Additional URL
https://library.smu.edu.sg/AI4ResearchWeek#programme
Publication Date
2024
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Library and Information Science Commons
Academic search and discovery tools in the age of AI and large language models: An overview of the space
Singapore
In the ever-evolving landscape of academic research, “AI tools” for literature search and synthesis are currently getting a lot of attention. These tools promise to ramp up productivity, enabling us to accomplish more in less time or absorb more knowledge without drowning in endless reading. With the sheer number of these systems increasing daily, it's natural to wonder: are they really worth our time and money? And if they are, how should we go about picking the right one from the multitude of options?
In this talk, I will share my views on how the space has developed over two increasingly popular but distinct set of what many call “AI tools”. The first is what I have dubbed “Citation based literature mapping tools”, which includes tools like Connected Papers, ResearchRabbit and Litmaps.
Secondly, the rise of transformer based large language (LLMs) models like OpenAI’s GPT models has also great implications on how academic discovery might change and in this talk I will highlight the 3 main ways in which the Natural language understanding and natural language understanding capabilities of LLMs are being used in currenct academic search engines such as Elicit, SciSpace and Scite assistant and the limits of such tools.
https://ink.library.smu.edu.sg/ai_research_week/Programme/Programme/1