no code implementations • 3 Jan 2024 • Haonan Wang, James Zou, Michael Mozer, Anirudh Goyal, Alex Lamb, Linjun Zhang, Weijie J Su, Zhun Deng, Michael Qizhe Xie, Hannah Brown, Kenji Kawaguchi
With the rise of advanced generative AI models capable of tasks once reserved for human creativity, the study of AI's creative potential becomes imperative for its responsible development and application.
no code implementations • 5 Dec 2023 • Xuan Long Do, Yiran Zhao, Hannah Brown, Yuxi Xie, James Xu Zhao, Nancy F. Chen, Kenji Kawaguchi, Michael Qizhe Xie, Junxian He
We propose a new method, Adversarial In-Context Learning (adv-ICL), to optimize prompt for in-context learning (ICL) by employing one LLM as a generator, another as a discriminator, and a third as a prompt modifier.
no code implementations • 10 Oct 2023 • Yang Zhang, Yawei Li, Hannah Brown, Mina Rezaei, Bernd Bischl, Philip Torr, Ashkan Khakzar, Kenji Kawaguchi
Feature attribution explains neural network outputs by identifying relevant input features.
no code implementations • 22 Jun 2023 • Xudong Shen, Hannah Brown, Jiashu Tao, Martin Strobel, Yao Tong, Akshay Narayan, Harold Soh, Finale Doshi-Velez
There is increasing attention being given to how to regulate AI systems.
no code implementations • 11 Feb 2022 • Hannah Brown, Katherine Lee, FatemehSadat Mireshghallah, Reza Shokri, Florian Tramèr
Language models lack the ability to understand the context and sensitivity of text, and tend to memorize phrases present in their training sets.