  Reframing a negative into a positive thought is at the crux of several
cognitive approaches to mental health and psychotherapy that could be made more
accessible by large language model-based solutions. Such reframing is typically
non-trivial and requires multiple rationalization steps to uncover the
underlying issue of a negative thought and transform it to be more positive.
However, this rationalization process is currently neglected by both datasets
and models which reframe thoughts in one step. In this work, we address this
gap by augmenting open-source datasets for positive text rewriting with
synthetically-generated Socratic rationales using a novel framework called
\textsc{SocraticReframe}. \textsc{SocraticReframe} uses a sequence of
question-answer pairs to rationalize the thought rewriting process. We show
that such Socratic rationales significantly improve positive text rewriting for
different open-source LLMs according to both automatic and human evaluations
guided by criteria from psychotherapy research.
