no code implementations • 31 Jul 2023 • Jessy Lin, Yuqing Du, Olivia Watkins, Danijar Hafner, Pieter Abbeel, Dan Klein, Anca Dragan
To interact with humans in the world, agents need to understand the diverse types of language that people use, relate them to the visual world, and act based on them.
1 code implementation • 31 May 2023 • Jessy Lin, Nicholas Tomlin, Jacob Andreas, Jason Eisner
In each of these settings, AI assistants and users have disparate abilities that they must combine to arrive at the best decision: assistants can access and process large amounts of information, while users have preferences and constraints external to the system.
1 code implementation • 20 Nov 2022 • Micah Carroll, Orr Paradise, Jessy Lin, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew Hausknecht, Anca Dragan, Sam Devlin
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks.
1 code implementation • NAACL 2022 • Jessy Lin, Geza Kovacs, Aditya Shastry, Joern Wuebker, John DeNero
We show that human errors in TEC exhibit a more diverse range of errors and far fewer translation fluency errors than the MT errors in automatic post-editing datasets, suggesting the need for dedicated TEC models that are specialized to correct human errors.
no code implementations • 28 Apr 2022 • Micah Carroll, Jessy Lin, Orr Paradise, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew Hausknecht, Anca Dragan, Sam Devlin
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks.
3 code implementations • 12 Apr 2022 • Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, Mike Lewis
Our model is the first generative model that is able to directly perform zero-shot code infilling, which we evaluate on challenging tasks such as type inference, comment generation, and variable re-naming.
Ranked #84 on Code Generation on MBPP
1 code implementation • ACL 2022 • Jessy Lin, Daniel Fried, Dan Klein, Anca Dragan
In classic instruction following, language like "I'd like the JetBlue flight" maps to actions (e. g., selecting that flight).
2 code implementations • ICML 2018 • Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin
Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model.
1 code implementation • 19 Dec 2017 • Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin
Second, we introduce a new algorithm to perform targeted adversarial attacks in the partial-information setting, where the attacker only has access to a limited number of target classes.