Publications (6)
Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast-Choose Three
Steven Reich, David Mueller, Nicholas Andrews
Modern neural networks do not always produce well-calibrated predictions, even when trained with a proper scoring function such as cross-entropy. In classification settings, simple…
Can Optimization Trajectories Explain Multi-Task Transfer?
David Mueller, Mark Dredze, Nicholas Andrews
Despite the widespread adoption of multi-task training in deep learning, little is understood about how multi-task learning (MTL) affects generalization. Prior work has conjectured…
Where does In-context Translation Happen in Large Language Models
Suzanna Sia, David Mueller, Kevin Duh
Self-supervised large language models have demonstrated the ability to perform Machine Translation (MT) via in-context learning, but little is known about where the model performs…
Uncertainty Distillation: Teaching Language Models to Express Semantic Confidence
Sophia Hager, David Mueller, Kevin Duh +1
As large language models (LLMs) are increasingly used for factual question-answering, it becomes more important for LLMs to have the capability to communicate the likelihood that t…
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?
David Mueller, Nicholas Andrews, Mark Dredze
Traditional multi-task learning architectures train a single model across multiple tasks through a shared encoder followed by task-specific decoders. Learning these models often re…
Sources of Transfer in Multilingual Named Entity Recognition
David Mueller, Nicholas Andrews, Mark Dredze
Named-entities are inherently multilingual, and annotations in any given language may be limited. This motivates us to consider polyglot named-entity recognition (NER), where one m…