Paris NLP saison 9 Meetup #1

📍8 rue Cambacérès, 75008 Paris
📆 October 9th, 7:00 p.m.
⚠️ Limited spots available. Be sure to reserve your place in advance!

👥 Alexandre Défossez - Chief Exploration Officer @ Kyutai
➡️ Moshi: a speech-text foundation model for real-time dialogue.
Summary: We will discuss Moshi, our recently released model. Moshi is capable of full-duplex dialogue, e.g. it can both speak and listen at any time, offering the most natural speech interaction to date. Besides, Moshi is also multimodal, in particular it is able to leverage its inner text monologue to improve the quality of its generation. We will cover the design choices behind Moshi in particular the efficient joint sequence modeling permitted by RQ-Transformer, and the use of large scale synthetic instruct data.

👥 Louis Lacombe, Valentin Laurent, Thibault Cordier - Data Scientist @ Quantmetry - Part of Capgemini Invent
➡️ Enhancing NLP Model Reliability with MAPIE: Conformal Prediction for Uncertainty Quantification
Summary: This talk introduces MAPIE, an open-source Python library designed to quantify uncertainties and control risks in machine learning models, with a focus on NLP applications. We will begin by discussing the importance of uncertainty quantification based on conformal prediction framework that ensures guarantees with few assumptions. Then, we will present MAPIE, showcasing how to compute conformal prediction sets for NLP tasks like text classification. Finally, we will explore practical use cases, highlighting the capabilities of MAPIE and providing attendees with a comprehensive overview of its potential applications.