190 citations · 215 across the 11 of their papers we have counts for
5 papers · 1 filter
Consistent Explanations in the Face of Model Indeterminacy via Ensembling
Dan Ley, Leonard Tang, Matthew Nazari +3
This work addresses the challenge of providing consistent explanations for predictive models in the presence of model indeterminacy, which arises due to the existence of multiple (…
Degraded Polygons Raise Fundamental Questions of Neural Network Perception
Leonard Tang, Dan Ley
It is well-known that modern computer vision systems often exhibit behaviors misaligned with those of humans: from adversarial attacks to image corruptions, deep learning vision mo…
Baselines for Identifying Watermarked Large Language Models
Leonard Tang, Gavin Uberti, Tom Shlomi
We consider the emerging problem of identifying the presence and use of watermarking schemes in widely used, publicly hosted, closed source large language models (LLMs). We introdu…
Learning the Wrong Lessons: Inserting Trojans During Knowledge Distillation
Leonard Tang, Tom Shlomi, Alexander Cai
In recent years, knowledge distillation has become a cornerstone of efficiently deployed machine learning, with labs and industries using knowledge distillation to train models tha…
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding
Steven H. Wang, Antoine Scardigli, Leonard Tang +7
Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address…