Development Innovation Research

Hi! PARIS Reading groups “Generative AI”

The Hi! PARIS reading groups propose to study a topic using scientific articles on a theoretical and a practical point of view. The reading groups are opportunities of interaction between our corporate donors and our affiliates academic teams around selected topics of interest.

Each Edition is planned for 2-4 sessions presenting one topic by the mean of 3-4 research papers. For each session: presentation of mathematical models and theoretical advances by a researcher + simulations with a Python notebook by an engineer.


Please register to the event using your professional email address to get your personal conference link. Please do not share your personalised link with others, it is unique to you. You will receive an email regarding your registration status.

Generative AI

Generative models are now a powerful tool for many applications in Artificial Intelligence such as Natural Language Processing (Chat GPT, Google Bard) or image and video generation Of course, these applications entail ethical and societal applications. The objective of this reading group is to provide a general overview of the generative models used for these two popular applications on one hand, and to discuss the challenges involved by their developement on the other hand.

The reading group will be divided in three sessions in which we will present each of these topics.

Session 1/3
Tuesday 9 January, 2024 – 2.00-3.30pm (Online) 


Matthieu Labeau, Télécom Paris – IP Paris 

GPT-3 to ChatGPT 

”Technical innovation” needed to go from GPT-3 to ChatGPT 

Session 2/3
Tuesday 14 November, 2023 – 2.00-3.30pm (Online) 


Julien RomeroTélécom SudParis – IP Paris 

Chain-of-thought prompting elicits reasoning in large language models.

This paper explores the concept of chain-of-thought in large language model, which consists in making explicit intermediate reasoning steps before giving the final answer.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., … & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824-24837.

Session 3/3
Tuesday 12 December, 2023 – 2.00-3.30pm (Online) 


Pablo M. Baquero, Hi! PARIS Chair Holder – HEC Paris

Different ethical and legal challenges involving foundation models (particularly under the AI Act).


France 2030

This work has benefited from a government grant managed by the ANR under France 2030 with the reference “ANR-22-CMAS-0002”.