Home // FSTM // News // Exploring the Potentials and Pitfalls of GPT Models in Scientific Research

Exploring the Potentials and Pitfalls of GPT Models in Scientific Research

twitter linkedin facebook email this page
Add to calendar
Speaker: Raul Ian Sosa (Faculty of Science Technology and Medicine, University of Luxembourg)
Event date: Wednesday, 22 March 2023 10:00 am - 11:00 am
Place: Fully virtual (contact Dr. Jakub Lengiewicz, jakub.lengiewicz@uni.lu, to register)


In recent months, GPT models have become increasingly popular as powerful tools for natural language processing tasks, such as summarization, language translation and text generation. In this presentation, we will provide an overview of GPT models, their architecture, and how they were trained. We will also discuss the potential applications of GPT models in scientific research, including their ability to generate hypotheses and assist with scientific writing. However, while GPT models have many promising uses, they also come with significant pitfalls, such as the potential to produce and propagate false statements. We will discuss these challenges, understand where they come from and explore ways to mitigate them in order to ensure that large natural language processing models of this kind can be used responsibly in scientific research.
* In compliance with current copyright laws we confirm that this abstract was generated by a human.

About the speaker: 

Raul Ian Sosa is a doctoral researcher in the Department of Physics and Materials Science, in the Faculty of Science Technology and Medicine of the University of Luxembourg.


The Machine Learning Seminar is a regular weekly seminar series aiming to harbour presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences. More information about the ML Seminar, together with video recordings from past meetings you will find here: https://legato-team.eu/seminars/