MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding

2 code implementations • 2 Jan 2023 • Steven H. Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dimitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets.

Reading Comprehension

imitation: Clean Imitation Learning Implementations

2 code implementations • 22 Nov 2022 • Adam Gleave, Mohammad Taufeeque, Juan Rocamonde, Erik Jenner, Steven H. Wang, Sam Toyer, Maximilian Ernestus, Nora Belrose, Scott Emmons, Stuart Russell

imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch.

Imitation Learning reinforcement-learning +1

Retrospective on the 2021 BASALT Competition on Learning from Human Feedback

no code implementations • 14 Apr 2022 • Rohin Shah, Steven H. Wang, Cody Wild, Stephanie Milani, Anssi Kanervisto, Vinicius G. Goecks, Nicholas Waytowich, David Watkins-Valls, Bharat Prakash, Edmund Mills, Divyansh Garg, Alexander Fries, Alexandra Souly, Chan Jun Shern, Daniel del Castillo, Tom Lieberum

The goal of the competition was to promote research towards agents that use learning from human feedback (LfHF) techniques to solve open-world tasks.

The MineRL BASALT Competition on Learning from Human Feedback

no code implementations • 5 Jul 2021 • Rohin Shah, Cody Wild, Steven H. Wang, Neel Alex, Brandon Houghton, William Guss, Sharada Mohanty, Anssi Kanervisto, Stephanie Milani, Nicholay Topin, Pieter Abbeel, Stuart Russell, Anca Dragan

Rather than training AI systems using a predefined reward function or using a labeled dataset with a predefined set of categories, we instead train the AI system using a learning signal derived from some form of human feedback, which can evolve over time as the understanding of the task changes, or as the capabilities of the AI system improve.

Imitation Learning