  Children with Autism Spectrum Disorder (ASD) often misunderstand social
situations and struggle to participate in daily routines. Psychology experts
write Social Stories under strict constraints of structural clarity,
descriptive orientation, and situational safety to enhance their abilities in
these regimes. However, Social Stories are costly in creation and often limited
in diversity and timeliness. As Large Language Models (LLMs) become
increasingly powerful, there is a growing need for more automated, affordable,
and accessible methods to generate Social Stories in real-time with broad
coverage. Adapting LLMs to meet the unique and strict constraints of Social
Stories is a challenging issue. To this end, we propose \textbf{SS-Bench}, a
\textbf{S}ocial \textbf{S}tory \textbf{Bench}mark for generating and evaluating
Social Stories. Specifically, we develop a constraint-driven strategy named
\textbf{\textsc{StarSow}} to hierarchically prompt LLMs to generate Social
Stories and build a benchmark, which has been validated through experiments to
fine-tune smaller models for generating qualified Social Stories. Additionally,
we introduce \textbf{Quality Assessment Criteria}, employed in human and GPT
evaluations, to verify the effectiveness of the generated stories. We hope this
work benefits the autism community and catalyzes future research focusing on
particular groups.
