Flexible Model Interpretability through Natural Language Model Editing
Karel D'Oosterlinck Thomas Demeester Chris Develder Christopher Potts
Model interpretability and model editing are crucial goals in the age of
large language models. Interestingly, there exists a link between these two
goals: if a method is able to systematically edit model behavior with regard to
a human concept of interest, this editor method can help make internal
representations more interpretable by pointing towards relevant representations
and systematically manipulating them.