4k citations · 13.4k across the 76 of their papers we have counts for
8 papers · 1 filter
Gemma 2: Improving Open Language Models at a Practical Size
Gemma Team, Morgane Riviere, Shreya Pathak +195
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In th…
Time Sensitive Knowledge Editing through Efficient Finetuning
Xiou Ge, Ali Mousavi, Edouard Grave +5
Large Language Models (LLMs) have demonstrated impressive capability in different tasks and are bringing transformative changes to many domains. However, keeping the knowledge in L…
Whispering Experts: Neural Interventions for Toxicity Mitigation in Language Models
Xavier Suau, Pieter Delobelle, Katherine Metcalf +4
An important issue with Large Language Models (LLMs) is their undesired ability to generate toxic language. In this work, we show that the neurons responsible for toxicity can be d…
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Huy V. Vo, Vasil Khalidov, Timothée Darcet +10
Self-supervised features are the cornerstone of modern machine learning systems. They are typically pre-trained on data collections whose construction and curation typically requir…
Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning
Théo Moutakanni, Piotr Bojanowski, Guillaume Chassagnon +7
AI Foundation models are gaining traction in various applications, including medical fields like radiology. However, medical foundation models are often tested on limited tasks, le…
Gemma: Open Models Based on Gemini Research and Technology
Gemma Team, Thomas Mesnard, Cassidy Hardin +105
This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate stro…