Expanding Creative Writing Beyond Simple Stories

Summary & Methodology Analysis

Language models often struggle with creative writing formats outside of simple storytelling because their training data is heavily story centric. This paper introduces an attribute guided genre expansion pipeline to improve how models handle structural, stylistic, functional, and formatting conventions of diverse creative formats. The methodology begins with source dataset selection using Reddit r/WritingPrompts as a seed dataset, followed by safety filtering and irrelevant content removal via GPT-5-mini. Step one involves thematic seed sampling, where n equals 5 query response pairs are randomly sampled for topical and stylistic breadth. Step two focuses on genre attribute sampling, collecting authoritative genre definitions, extracting structured attribute lists using GPT-5, manually refining them to 15 attributes per genre, and randomly sampling a subset to control instruction specificity. Step three applies attribute guided pair synthesis, prompting GPT-5-mini with a meta prompt that combines the target genre, task instructions, sampled genre attributes, and few shot examples with verbalized sampling to produce queries. Responses are generated using Qwen3-235B-A22B-Thinking, and quality filtered using Qwen3-30B-A3B-Instruct as an LLM as a judge. Fine tuning models on the resulting Multi-Genre Collection yields an Arena Hard score of 33.0, a WritingBench score of 60.6, and a Multi-Genre score of 68.4 when using the Llama model. Furthermore, increasing the number of training genres from 0 to all 13 genres raises the novelty score monotonically from 3.87 to 4.81. The paper does not specify any computational limitations or hardware resource requirements.