Selective Rationalization

Turn the rationales of single examples into a global explanation, such as a knowledge base.

Study whether a mixture of experts model can address interlocking in selective rationalization.

Evaluate whether large language models can perform selective rationalization through prompting.

Extract rationales from structured representations of text, such as trees, under structural constraints.

GenSPP trains the generator and predictor of a self-explaining text classifier separately with genetic search, removing interlocking and producing more accurate highlights.