Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Draw Me a Flower: Grounding Formal Abstract Structures Stated in Informal Natural Language
Forming and interpreting abstraction is a core process in human communication. In particular, when giving and performing complex instructions stated in natural language (NL), people may naturally…
Extracting Latent Steering Vectors from Pretrained Language Models
Prior work on controllable text generation has focused on learning how to control language models through trainable decoding, smart-prompt design, or fine-tuning based on a desired objective. We…
Generated Knowledge Prompting for Commonsense Reasoning
Despite their ability to capture large amount of knowledge during pretraining, large-scale language models often benefit from incorporating external knowledge bases, especially on commonsense…
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets
Natural language processing models often exploit spurious correlations between task-independent features and labels in datasets to perform well only within the distributions they are trained on,…
Generating Scientific Definitions with Controllable Complexity
Unfamiliar terminology and complex language can present barriers to understanding science. Natural language processing stands to help address these issues by automatically defining unfamiliar terms.…
Generating Scientific Claims for Zero-Shot Scientific Fact Checking
Automated scientific fact checking is difficult due to the complexity of scientific language and a lack of significant amounts of training data, as annotation requires domain expertise. To address…
Hey AI, Can You Solve Complex Tasks by Talking to Agents?
Humans often solve complex problems by interacting (in natural language) with existing agents, such as AI assistants, that can solve simpler sub-tasks. These agents themselves can be powerful…
Is GPT-3 Text Indistinguishable from Human Text? SCARECROW: A Framework for Scrutinizing Machine Text
Modern neural text generation systems can produce remarkably fluent and grammatical texts. While earlier language models suffered from repetition and syntactic errors, the errors made by contemporary…
Large Scale Substitution-based Word Sense Induction
We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is…
NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks
Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple calculations is an important skill of AI systems. While many datasets and models have been developed to this…