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

Most creative writing datasets are, at their core, story datasets. The structural conventions of rap, game design documents, screenplays, and song lyrics formatted for tools like Suno are simply absent. A model trained on that narrow base can produce rich narrative prose while completely failing to follow the formal expectations of a genre it has never seen structured data for.

This paper's central argument is that thematic diversity and genre-form fidelity are two separate problems, and conflating them is why generic synthetic data pipelines fall short for creative writing. The solution proposed here separates them explicitly. Human-authored prompts from Reddit's r/WritingPrompts supply topical breadth—the "what to write about" dimension. Manually curated genre attributes, drawn from encyclopedic sources and creative writing manuals and then hand-reviewed, supply the structural constraints—the "how this genre actually works" dimension. Combining them via a meta-prompt produces queries that are both thematically varied and genre-faithful.

The resulting corpus, the Multi-Genre Collection, spans 13 creative genres in 50 thousand examples. Attribute sampling controls instruction specificity: when zero attributes are sampled, a query is open-ended; as more are added, the prompt becomes progressively more constrained. This mirrors how real writing requests actually arrive—some vague, some highly specified.

The qualitative example in the paper is worth dwelling on. Given a prompt for story-driven symphonic metal deathcore lyrics formatted for Suno, the base Llama model produces a generic verse-chorus structure with no title, no named entities, no timestamps. The fine-tuned version produces a titled composition set in a named world, with a recurring character, specific named spells, and section-level timestamps matching Suno's expected format. That is not a marginal improvement in fluency—it is the difference between a model that understands genre conventions and one that does not.

The ablation on genre count is the paper's most important empirical finding. Novelty scores, measured using a metric that clusters semantically equivalent outputs to estimate distinctness, rise monotonically as training genres increase from zero to all 13. Genre diversity does not just improve performance on held-out genre tasks; it improves the model's general capacity to produce varied, non-redundant outputs. That generalization effect is the real argument for this approach.

Fair-comparison experiments hold dataset size constant at 2 thousand examples per corpus, and the Multi-Genre Collection still substantially outperforms both DeepWriting-20k and LongWriter-6k across all benchmarks. The gains are largest on Arena Hard, which is the most out-of-distribution test. Human evaluation on 50 WritingBench prompts corroborates the LLM-judge results, addressing the obvious concern about self-preference bias from GPT-5-mini appearing on both sides of the evaluation.

The dependency on strong proprietary models—GPT-5-mini for filtering and query generation, Qwen3-235B for response generation—is a real constraint on reproducibility. The manual curation of genre attributes is also not a step that scales automatically to new genres. But the core insight, that genre-form control requires human-defined structural constraints rather than just more data, is well-supported and practically actionable.