Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs
Can we teach natural language understanding models to track their beliefs through intermediate points in text? We propose a representation learning framework called breakpoint modeling that allows…
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts
Explicit decomposition modeling, which involves breaking down complex tasks into more straightforward and often more interpretable sub-tasks, has long been a central theme in developing robust and…
Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE
Figurative language (e.g., “he flew like the wind”) is challenging to understand, as it is hard to tell what implicit information is being conveyed from the surface form alone. We hypothesize that…
SciFact-Open: Towards open-domain scientific claim verification
While research on scientific claim verification has led to the development of powerful systems that appear to approach human performance, these approaches have yet to be tested in a realistic…
Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs
Social intelligence and Theory of Mind (T O M), i.e., the ability to reason about the different mental states, intents, and reactions of all people involved, allow humans to effectively navigate and…
What Language Model to Train if You Have One Million GPU Hours?
The crystallization of modeling methods around the Transformer architecture has been a boon for practitioners. Simple, well-motivated architectural variations that transfer across tasks and scale,…
ProsocialDialog: A Prosocial Backbone for Conversational Agents
Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog,…
Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations
Despite their impressive capabilities, large pretrained language models (LMs) struggle with consistent reasoning; recently, prompting LMs to generate explanations that self-guide the inference has…
Specializing Multilingual Language Models: An Empirical Study
Pretrained multilingual language models have become a common tool in transferring NLP capabilities to low-resource languages, often with adaptations. In this work, we study the performance,…
Towards Personalized Descriptions of Scientific Concepts
A single scientific concept can be described in many different ways, and the most informative description depends on the audience. In this paper, we propose generating personalized scientific…