no code implementations • LREC 2014 • Heather Pon-Barry, Stuart Shieber, Nicholas Longenbaugh
In such applications, we would like to know the speaker{'}s actual level of certainty, but past research has only revealed listeners{'} perception of the speaker{'}s level of certainty.
no code implementations • NeurIPS 2020 • Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, Stuart Shieber
As a case study, we apply this methodology to analyzing gender bias in pre-trained Transformer language models.
no code implementations • 29 Sep 2021 • Simas Sakenis, Stuart Shieber
Specifically, while learning a direct mapping from inputs to outputs is feasible for System 1 tasks, we argue that algorithmic System 2 tasks can only be solved by learning a mapping from inputs to outputs through a series of intermediate steps.
1 code implementation • ACL 2021 • Matthew Finlayson, Aaron Mueller, Sebastian Gehrmann, Stuart Shieber, Tal Linzen, Yonatan Belinkov
Targeted syntactic evaluations have demonstrated the ability of language models to perform subject-verb agreement given difficult contexts.
1 code implementation • WS 2020 • Abdelrhman Saleh, Tovly Deutsch, Stephen Casper, Yonatan Belinkov, Stuart Shieber
The predominant approach to open-domain dialog generation relies on end-to-end training of neural models on chat datasets.
1 code implementation • WS 2020 • Tovly Deutsch, Masoud Jasbi, Stuart Shieber
Readability assessment aims to automatically classify text by the level appropriate for learning readers.
1 code implementation • 26 Apr 2020 • Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Simas Sakenis, Jason Huang, Yaron Singer, Stuart Shieber
Common methods for interpreting neural models in natural language processing typically examine either their structure or their behavior, but not both.
1 code implementation • 2 Nov 2023 • Yuntian Deng, Kiran Prasad, Roland Fernandez, Paul Smolensky, Vishrav Chaudhary, Stuart Shieber
In this work, we explore an alternative reasoning approach: instead of explicitly producing the chain of thought reasoning steps, we use the language model's internal hidden states to perform implicit reasoning.