8 citations · 9 across the 5 of their papers we have counts for
5 papers
Compress-Distill: Reasoning Trace Compression for Efficient Knowledge Distillation
Maxime Griot, Paul Steven Scotti, Tanishq Mathew Abraham
Reasoning models produce long chain-of-thought traces that are costly to distill and encourage verbose student outputs. We study post-hoc compression of such traces before knowledg…
torchtune: PyTorch native post-training library
Mark Obozov, Maxime Griot, Joseph Cummings +8
Modern LLMs typically require multistage training pipelines to achieve strong downstream performance, with post-training serving as the main interface for adapting open-weight mode…
Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks
Benjamin Warner, Ratna Sagari Grandhi, Max Kieffer +32
Evaluating large language models (LLMs) for medical applications remains challenging due to benchmark saturation, limited data accessibility, and insufficient coverage of relevant…
MedRiskEval: Medical Risk Evaluation Benchmark of Language Models, On the Importance of User Perspectives in Healthcare Settings
Jean-Philippe Corbeil, Minseon Kim, Maxime Griot +4
As the performance of large language models (LLMs) continues to advance, their adoption in the medical domain is increasing. However, most existing risk evaluations largely focused…
Pattern Recognition or Medical Knowledge? The Problem with Multiple-Choice Questions in Medicine
Maxime Griot, Jean Vanderdonckt, Demet Yuksel +1
Large Language Models (LLMs) such as ChatGPT demonstrate significant potential in the medical domain and are often evaluated using multiple-choice questions (MCQs) modeled on exams…