AURA: Natural Language Reasoning for Aleatoric Uncertainty in Rationales
Hazel Kim
Rationales behind answers not only explain model decisions but boost language
models to reason well on complex reasoning tasks. However, obtaining impeccable
rationales is often impossible. Besides, it is non-trivial to estimate the
degree to which the rationales are faithful enough to encourage model
performance. Thus, such reasoning tasks often compel models to output correct
answers under undesirable rationales and are sub-optimal compared to what the
models are fully capable of. In this work, we propose how to deal with
imperfect rationales causing aleatoric uncertainty. We first define the
ambiguous rationales with entropy scores of given rationales, using model prior
beliefs as informativeness. We then guide models to select one of two different
reasoning models according to the ambiguity of rationales. We empirically argue
that our proposed method produces robust performance superiority against the
adversarial quality of rationales and low-resource settings.