26 citations · 107 across the 12 of their papers we have counts for
13 papers · 1 filter
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment
Zhijing Jin, Sydney Levine, Fernando Gonzalez +6
AI systems are becoming increasingly intertwined with human life. In order to effectively collaborate with humans and ensure safety, AI systems need to be able to understand, inter…
Aligning to Social Norms and Values in Interactive Narratives
Prithviraj Ammanabrolu, Liwei Jiang, Maarten Sap +2
We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games -- environments wherein an agent perceives…
Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts
Ashutosh Baheti, Maarten Sap, Alan Ritter +1
Dialogue models trained on human conversations inadvertently learn to generate toxic responses. In addition to producing explicitly offensive utterances, these models can also impl…
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts
Alisa Liu, Maarten Sap, Ximing Lu +4
Despite recent advances in natural language generation, it remains challenging to control attributes of generated text. We propose DExperts: Decoding-time Experts, a decoding-time…
Detoxifying Language Models Risks Marginalizing Minority Voices
Albert Xu, Eshaan Pathak, Eric Wallace +3
Language models (LMs) must be both safe and equitable to be responsibly deployed in practice. With safety in mind, numerous detoxification techniques (e.g., Dathathri et al. 2020;…
Challenges in Automated Debiasing for Toxic Language Detection
Xuhui Zhou, Maarten Sap, Swabha Swayamdipta +2
Biased associations have been a challenge in the development of classifiers for detecting toxic language, hindering both fairness and accuracy. As potential solutions, we investiga…