M. Paganini

Prune Responsibly

2020 · arXiv (Cornell University)

Irrespective of the specific definition of fairness in a machine learning application, pruning the underlying model affects it. We investigate and document the emergence and exacerbation of undesirable per-class performance imbalances, across tasks and architectures, …

Improving language models by retrieving from trillions of tokens

2021 · arXiv (Cornell University)

We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a $2$ trillion token database, our Retrieval-Enhanced Transformer (RETRO) obtains comparable performance …

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

2021 · arXiv (Cornell University)

Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis of Transformer-based language model …