SS-GEN: A Social Story Generation Framework with Large Language Models
<|reference_start|>SS-GEN: A Social Story Generation Framework with Large Language Models: Children with Autism Spectrum Disorder (ASD) often misunderstand social situations and struggle to participate in daily routines. Social Stories are traditionally crafted by psychology experts under strict constraints to address these challenges but are costly and limited in diversity. As Large Language Models (LLMs) advance, there's an opportunity to develop more automated, affordable, and accessible methods to generate Social Stories in real-time with broad coverage. However, adapting LLMs to meet the unique and strict constraints of Social Stories is a challenging issue. To this end, we propose \textbf{SS-GEN}, a \textbf{S}ocial \textbf{S}tory \textbf{GEN}eration framework with LLMs. Firstly, we develop a constraint-driven sophisticated strategy named \textbf{\textsc{StarSow}} to hierarchically prompt LLMs to generate Social Stories at scale, followed by rigorous human filtering to build a high-quality dataset. Additionally, we introduce \textbf{quality assessment criteria} to evaluate the effectiveness of these generated stories. Considering that powerful closed-source large models require very complex instructions and expensive API fees, we finally fine-tune smaller language models with our curated high-quality dataset, achieving comparable results at lower costs and with simpler instruction and deployment. This work marks a significant step in leveraging AI to personalize Social Stories cost-effectively for autistic children at scale, which we hope can encourage future research. The prompt, code and data will release in the \texttt{Technical Appendix} and \texttt{Code \& Data Appendix} at \url{https://github.com/MIMIFY/SS-GEN}.<|reference_end|>
@article{feng2024ss-gen:,
    title={SS-GEN: A Social Story Generation Framework with Large Language Models},
    author={Yi Feng, Mingyang Song, Jiaqi Wang, Zhuang Chen, Guanqun Bi, Minlie
  Huang, Liping Jing, Jian Yu},
    journal={arXiv preprint arXiv:2406.15695},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15695},
    primaryClass={cs.CL}
}