24 citations · 95 across the 28 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…
Causal Abstractions of Neural Networks
Atticus Geiger, Hanson Lu, Thomas Icard +1
Structural analysis methods (e.g., probing and feature attribution) are increasingly important tools for neural network analysis. We propose a new structural analysis method ground…
Dynabench: Rethinking Benchmarking in NLP
Douwe Kiela, Max Bartolo, Yixin Nie +16
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop datase…