Paper #4: Multi-Genre Collection: Scaling Creative Writing Beyond Story-Centric Data

Creative writing for large language models (LLMs) is often treated as synonymous with story generation, neglecting the distinct structural and functional constraints of other practically important genres. We introduce the Multi-Genre Collection, a 50K-instance dataset spanning 13 creative writing genres, designed to broaden coverage beyond stories while preserving diversity in themes and instruction specificity. To construct the dataset, we propose an attribute-guided query generation pipeline that transfers topical breadth from human-authored story prompts into genre-faithful queries using 5–15 manually curated attributes per genre. Concretely, we sample few-shot demonstrations from filtered r/WritingPrompts and inject genre attributes to control constraint granularity. One LLM transforms these thematic seeds into diverse, structure-aware queries, while a separate generation model produces the corresponding high-quality responses. We confirm balanced genre coverage and clear semantic separation in the embedding space using t-SNE analyses, which validates the distinctiveness of the curated genres. Empirically, models fine-tuned on our dataset consistently outperform base models and writing-specialized baselines. Furthermore, we demonstrate that increasing genre diversity significantly boosts output novelty. These results establish genre diversity as a foundational driver for robust creative writing capabilities.