Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constraints, however,…
Time Waits for No One! Analysis and Challenges of Temporal Misalignment
When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance. In this work, we…
Transparent Human Evaluation for Image Captioning
We establish a rubric-based human evaluation protocol for image captioning models. Our scoring rubrics and their definitions are carefully developed based on machineand humangenerated captions on…
Data Governance in the Age of Large-Scale Data-Driven Language Technology
The recent emergence and adoption of Machine Learning technology, and specifically of Large Language Models, has drawn attention to the need for systematic and transparent management of language…
Measuring the Carbon Intensity of AI in Cloud Instances
The advent of cloud computing has provided people around the world with unprecedented access to computational power and enabled rapid growth in technologies such as machine learning, the…
Domain Mismatch Doesn’t Always Prevent Cross-Lingual Transfer Learning
Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised…
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,…
Retrieval Data Augmentation Informed by Downstream Question Answering Performance
Training retrieval models to fetch contexts for Question Answering (QA) over large corpora requires labeling relevant passages in those corpora. Since obtaining exhaustive manual annotations of all…
NaturalProver: Grounded Mathematical Proof Generation with Language Models
Theorem proving in natural mathematical language - the mixture of symbolic and natural language used by humans - plays a central role in mathematical advances and education, and tests aspects of…
Zero- and Few-Shot NLP with Pretrained Language Models
The ability to efficiently learn from little-to-no data is critical to applying NLP to tasks where data collection is costly or otherwise difficult. This is a challenging setting both academically…