Scaling Creative Writing Beyond Story-Centric Data with Attribute-Guided Genre Expansion

Summary

High-quality creative writing data for LLMs is dominated by story-centric data, limiting models' adherence to diverse creative formats. The authors propose an attribute-guided genre expansion framework that uses human-authored story prompts as creative seeds and manually curated genre attributes to enforce structural, stylistic, and formatting conventions. This produces genre-faithful query-response pairs, which are quality-filtered. The resulting Multi-Genre Collection contains 50K examples across 13 genres, including story, rap, lyrics, scripts, game design, and character design. Experiments on out-of-distribution writing benchmarks and held-out genre diagnostics show that models fine-tuned on this data outperform baselines and models trained on existing writing corpora. Genre-count ablations indicate that controlled genre expansion, rather than story-centric scaling alone, is key to robust creative writing capability.