6 citations · 17 across the 12 of their papers we have counts for
4 papers · 1 filter
Causal Distillation for Language Models
Zhengxuan Wu, Atticus Geiger, Josh Rozner +5
Distillation efforts have led to language models that are more compact and efficient without serious drops in performance. The standard approach to distillation trains a student mo…
Inducing Causal Structure for Interpretable Neural Networks
Atticus Geiger, Zhengxuan Wu, Hanson Lu +5
In many areas, we have well-founded insights about causal structure that would be useful to bring into our trained models while still allowing them to learn in a data-driven fashio…
ReaSCAN: Compositional Reasoning in Language Grounding
Zhengxuan Wu, Elisa Kreiss, Desmond C. Ong +1
The ability to compositionally map language to referents, relations, and actions is an essential component of language understanding. The recent gSCAN dataset (Ruis et al. 2020, Ne…
Concadia: Towards Image-Based Text Generation with a Purpose
Elisa Kreiss, Fei Fang, Noah D. Goodman +1
Current deep learning models often achieve excellent results on benchmark image-to-text datasets but fail to generate texts that are useful in practice. We argue that to close this…