2 citations · 4 across the 18 of their papers we have counts for
4 papers · 1 filter
A Systematic Examination of Preference Learning through the Lens of Instruction-Following
Joongwon Kim, Anirudh Goyal, Aston Zhang +6
Preference learning is a widely adopted post-training technique that aligns large language models (LLMs) to human preferences and improves specific downstream task capabilities. In…
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…
Narrowing the Focus: Learned Optimizers for Pretrained Models
Gus Kristiansen, Mark Sandler, Andrey Zhmoginov +4
In modern deep learning, the models are learned by applying gradient updates using an optimizer, which transforms the updates based on various statistics. Optimizers are often hand…
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…