Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
Large language models excel at a variety of language tasks when prompted with examples or instructions. Yet controlling these models through prompting alone is limited. Tailoring language models…
Language Models with Rationality
While large language models (LLMs) are proficient at question-answering (QA), the dependencies between their answers and other "beliefs" they may have about the world are typically unstated, and may…
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)…
Measuring and Narrowing the Compositionality Gap in Language Models
We investigate the ability of language models to perform compositional reasoning tasks where the overall solution depends on correctly composing the answers to sub-problems. We measure how often…
PaperMage: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents
Despite growing interest in applying natural language processing (NLP) and computer vision (CV) models to the scholarly domain, scientific documents remain challenging to work with. They’re often in…
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…
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…