Training language models to follow instructions with human feedback

7 code implementations • 4 Mar 2022 • Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, Ryan Lowe

In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback.

WebGPT: Browser-assisted question-answering with human feedback

2 code implementations • 17 Dec 2021 • Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, John Schulman

This model's answers are preferred by humans 56% of the time to those of our human demonstrators, and 69% of the time to the highest-voted answer from Reddit.

Imitation Learning Navigate +1

Auto-weighted Multi-view Feature Selection with Graph Optimization

no code implementations • 11 Apr 2021 • Qi Wang, Xu Jiang, Mulin Chen, Xuelong Li

In this paper, we focus on the unsupervised multi-view feature selection which tries to handle high dimensional data in the field of multi-view learning.

feature selection Graph Learning +1

Semi-Federated Scheduling of Parallel Real-Time Tasks on Multiprocessors

no code implementations • 9 May 2017 • Xu Jiang, Nan Guan, Xiang Long, Wang Yi

In this paper we propose the semi-federate scheduling approach, which only grants $x$ dedicated processors to a heavy task with processing capacity requirement $x + \epsilon$, and schedules the remaining $\epsilon$ part together with light tasks on shared processors.

Distributed, Parallel, and Cluster Computing