Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Machine Reading Comprehension using Case-based Reasoning
We present an accurate and interpretable method for answer extraction in machine reading comprehension that is reminiscent of case-based reasoning (CBR) from classical AI. Our method (CBR-MRC)…
SHARCS: Efficient Transformers through Routing with Dynamic Width Sub-networks
We introduce SHARCS for adaptive inference that takes into account the hardness of input samples. SHARCS can train a router on any transformer network, enabling the model to direct different samples…
"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…
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…
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…
LongBoX: Evaluating Transformers on Long-Sequence Clinical Tasks
Many large language models (LLMs) for medicine have largely been evaluated on short texts, and their ability to handle longer sequences such as a complete electronic health record (EHR) has not been…
The Alignment Ceiling: Objective Mismatch in Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique to make large language models (LLMs) easier to prompt and more capable in complex settings. RLHF at its core is…