no code implementations • Findings (ACL) 2022 • Takateru Yamakoshi, Thomas Griffiths, Robert Hawkins
Sampling is a promising bottom-up method for exposing what generative models have learned about language, but it remains unclear how to generate representative samples from popular masked language models (MLMs) like BERT.
no code implementations • 16 Nov 2023 • Yun-Shiuan Chuang, Siddharth Suresh, Nikunj Harlalka, Agam Goyal, Robert Hawkins, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy T. Rogers
Human groups are able to converge on more accurate beliefs through deliberation, even in the presence of polarization and partisan bias -- a phenomenon known as the "wisdom of partisan crowds."
1 code implementation • 16 Nov 2023 • Yun-Shiuan Chuang, Agam Goyal, Nikunj Harlalka, Siddharth Suresh, Robert Hawkins, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy T. Rogers
Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation.
1 code implementation • 3 May 2022 • Julia White, Noah Goodman, Robert Hawkins
Language use differs dramatically from context to context.
no code implementations • EMNLP 2021 • Julia White, Gabriel Poesia, Robert Hawkins, Dorsa Sadigh, Noah Goodman
An overarching goal of natural language processing is to enable machines to communicate seamlessly with humans.
1 code implementation • 11 Mar 2019 • Judith Fan, Robert Hawkins, Mike Wu, Noah Goodman
On each trial, both participants were shown the same four objects, but in different locations.