talk-data.com

📍GitGuardian office, 12 rue d'Aboukir, 75002 📆 March 5th, 7:00 p.m.

👥 Johan Leduc Senior ML Engineer @ GitGuardian ➡️ Uncovering Critical Secrets with LLMs Summary: GitGuardian detects secrets—like passwords and API keys—in code, but the sheer volume can overwhelm users. Sifting through them to find the most critical ones is like searching for a needle in a haystack. In this talk, we’ll dive into how we leveraged LLMs at key stages to prioritize secrets efficiently and at scale.

👥 Louis Leconte - ML Research Engineer @Pruna AI ➡️ How to quantize a LLM in 3 lines of code Summary: Large Language Models (LLMs) are powerful but often computationally expensive, making deployment challenging. In this talk, we’ll explore how to quantize an LLM in just three lines of code using Pruna AI’s frictionless solution. I’ll introduce our data-free vector quantization approach, which optimizes CUDA kernels to enable efficient inference—all in under five minutes. Whether you're working on edge AI, server-side deployments, or simply curious about making LLMs more efficient, this session will give you a hands-on glimpse into state-of-the-art quantization techniques.

Perceval Wajsburt - APHP

Multilingual normalization and structured entity composition in medical documents

Hospital clinical documents are a true treasure trove of information for various applications, from clinical research to epidemiological surveillance, medical coding, and decision support. However, their use for large-scale computer processing is hampered by the fact that they are predominantly written in natural language, requiring prior structuring. In this seminar, we will primarily focus on two tasks to structure these documents: entity normalization and structured entity extraction. We propose a large-scale multilingual approach to normalize named entities in multiple languages. Then, we explain how to compose simple entities into structured entities, using a novel method based on mention cliques and scope relations. Our evaluation is based on a new annotated corpus of clinical breast radiography reports. We will also discuss the challenges associated with applying deep learning in conditions of limited data, for languages other than English and in the clinical field.

Etienne Bernard - NuMind

Creating NLP Models in the Age of LLMs

Large Language Models (LLMs) have the potential to radically transform the way we tackle NLP applications, but it is still unclear how to use them best. We recently developed NuMind, a tool that leverages LLMs to efficiently create NLP models (e.g. classifiers and entity detectors) through a paradigm inspired by how humans teach each other, which we call Interactive AI Development. In this talk, I will present this paradigm, demonstrate the tool that we developed around it, and talk about the scientific & technological solutions used under the hood.