2 citations · 8 across the 13 of their papers we have counts for
4 papers · 1 filter
LLäMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch
Jan Pfister, Julia Wunderle, Andreas Hotho
We create two German-only decoder models, LLäMmlein 120M and 1B, transparently from scratch and publish them, along with the training data, for the German NLP research community to…
Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks
Samuele Poppi, Zheng-Xin Yong, Yifei He +4
Recent advancements in Large Language Models (LLMs) have sparked widespread concerns about their safety. Recent work demonstrates that safety alignment of LLMs can be easily remove…
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation
Louis L. Chen, Bobbie Chern, Eric Eckstrand +2
Labeling errors in datasets are common, arising in a variety of contexts, such as human labeling, noisy labeling, and weak labeling (i.e., image classification). Although neural ne…