SS-GEN: A Social Story Generation Framework with Large Language Models (AAAI 2025 Oral)

🧠 Introduction

SS-GEN is a novel framework for automated generation of Social Stories™ aimed at helping children with Autism Spectrum Disorder (ASD) better understand and navigate social situations. Social Stories have traditionally been written by psychologists under strict guidelines — however, this process is costly, time-consuming, and lacks diversity.

SS-GEN leverages Large Language Models (LLMs) and a custom-designed, constraint-driven strategy (STARSOW) to generate personalized, high-quality Social Stories at scale.

📝 Abstract

Children with Autism Spectrum Disorder (ASD) often struggle to interpret social cues and engage in daily routines. Social Stories™, designed to improve these abilities, are typically handcrafted by experts, limiting their scalability. To address this, we propose SS-GEN, a framework that prompts LLMs to generate constraint-compliant Social Stories using a novel strategy named STARSOW. We further curate a high-quality dataset via human filtering and propose a structured evaluation framework. Finally, we fine-tune lightweight open-source models on our dataset, achieving strong results with lower cost and easier deployment. SS-GEN represents a significant step in creating accessible, affordable, and automated tools to assist ASD communities.

🌳 Framework: STARSOW

The STARSOW pipeline includes:

1. Taking Root: Generating diverse chapter themes from seed examples.
2. Branching Out: Producing multiple Social Story titles under each chapter.
3. Bearing Star Fruits: Completing full stories from titles, guided by strict structural and narrative constraints.
4. Gardening Work: Rigorous filtering to ensure quality, relevance, and safety.

📊 Dataset

We construct a large-scale Social Story dataset:

Item	Description
Chapters	57 diverse themes
Titles in each chapter	>=70
Total stories	5,085
Avg. chapter length (in words)	2.46 words
Avg. title length (in words)	5.28
Avg. story content length (in words)	281.65
Structure	Title + Introduction + Body + Conclusion
Constraints	Structural Clarity, Descriptive Orientation, Situational Safety

🧪 Load Dataset via Hugging Face 🤗 (Recommend)

from datasets import load_dataset

# Login using e.g. `huggingface-cli login` to access this dataset
ds = load_dataset("FMiMiY/SS-GEN")

🔗 View on Hugging Face

📈 Results

• We fine-tuned several 2B–8B models (e.g., Gemma, Mistral, LLaMA3).
• Fine-tuned models significantly outperformed zero-shot baselines across BLEU, ROUGE, and BERTScore.
• Human evaluation confirmed improvements in empathy, coherence, and narrative safety.

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📁 Repository Structure

📂 SS-GEN/
├── SS-GEN Dataset/         # Dataset (Available now)
├── model/                  # Coming soon
├── code /                  # Coming soon
├── README.md
├── Technical Appendix.pdf  # Details includ Prompt templates
└── ...

Citation

If you use SS-GEN or our dataset, please cite:

@article{feng2024ss,
  title={SS-GEN: A Social Story Generation Framework with Large Language Models},
  author={Feng, Yi and Song, Mingyang and Wang, Jiaqi and Chen, Zhuang and Bi, Guanqun and Huang, Minlie and Jing, Liping and Yu, Jian},
  journal={arXiv preprint arXiv:2406.15695},
  year={2024}
}

🤝 Acknowledgments

This work is supported by Beijing Jiaotong University, Tsinghua University, and Tencent. Special thanks to psychologists, educators, and collaborators who helped shape and evaluate this project.

📬 Contact

If you have any questions, suggestions or feedback, feel free to submmit a issue or contact :

Yi Feng – [email protected]