Geoffrey Irving

Fine-Tuning Language Models from Human Preferences

2019 · arXiv (Cornell University)

Reward learning enables the application of reinforcement learning (RL) to tasks where reward is defined by human judgment, building a model of reward by asking humans questions. Most work on reward learning has used simulated …

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 …

Red Teaming Language Models with Language Models

2022

Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, Geoffrey Irving. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.