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Causal Distillation for Language Models
Distillation efforts have led to language models that are more compact and efficient without serious drops in performance.
Inducing Causal Structure for Interpretable Neural Networks
In IIT, we (1) align variables in a causal model (e. g., a deterministic program or Bayesian network) with representations in a neural model and (2) train the neural model to match the counterfactual behavior of the causal model on a base input when aligned representations in both models are set to be the value they would be for a source input.
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