Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization
We present SODA : the first publicly available, million-scale high-quality social dialogue dataset. Using SODA , we train COSMO : a generalizable conversation agent outperforming previous…
TaskWeb: Selecting Better Source Tasks for Multi-task NLP
Recent work in NLP has shown promising results in training models on large amounts of tasks to achieve better generalization. However, it is not well-understood how tasks are related, and how…
Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements
Despite the much discussed capabilities of today's language models, they are still prone to silly and unexpected commonsense failures. We consider a retrospective verification approach that reflects…
We're Afraid Language Models Aren't Modeling Ambiguity
Ambiguity is an intrinsic feature of natural language. Managing ambiguity is a key part of human language understanding, allowing us to anticipate misunderstanding as communicators and revise our…
What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations
Moral or ethical judgments rely heavily on the specific contexts in which they occur. Understanding varying shades of defeasible contextualizations (i.e., additional information that strengthens or…
"You Are An Expert Linguistic Annotator": Limits of LLMs as Analyzers of Abstract Meaning Representation
Large language models (LLMs) show amazing proficiency and fluency in the use of language. Does this mean that they have also acquired insightful linguistic knowledge about the language, to an extent…
Localized Symbolic Knowledge Distillation for Visual Commonsense Models
Instruction following vision-language (VL) models offer a flexible interface that supports a broad range of multimodal tasks in a zero-shot fashion. However, interfaces that operate on full images…
RCT Rejection Sampling for Causal Estimation Evaluation
Confounding is a significant obstacle to unbiased estimation of causal effects from observational data. For settings with high-dimensional covariates -- such as text data, genomics, or the…
CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies
Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items. Examples include generating entailment graphs, hierarchical cross-document coreference…
CARE: Extracting Experimental Findings From Clinical Literature
Extracting fine-grained experimental findings from literature can provide massive utility for scientific applications. Prior work has focused on developing annotation schemas and datasets for…