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Interactive Analysis of LLMs using Meaningful Counterfactuals
First, the generated textual counterfactuals should be meaningful and readable to users and thus can be mentally compared to draw conclusions.
A Theoretical Result on the Inductive Bias of RNN Language Models
However, a closer inspection of Hewitt et al.'s (2020) construction shows that it is not limited to hierarchical LMs, posing the question of what \emph{other classes} of LMs can be efficiently represented by RNNs.
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