以屬性引導的文類擴展：超越以故事為中心數據的創意寫作規模化

摘要

高品質的創意寫作數據對於大型語言模型（LLMs）而言，仍以故事為中心的數據為主，這限制了模型遵循多樣化創意格式之結構與功能慣例的能力。我們提出一個屬性引導的體裁擴展框架，用以將創意寫作數據擴展至故事生成之外。透過將主題廣度與體裁形式控制分離，我們的框架利用人工撰寫的故事提示作為多樣化的創意種子，同時運用人工策劃的體裁屬性，以確保其遵循獨特的結構、文體與格式規範。我們將這些元素結合，提示強大的LLMs生成符合體裁的查詢-回應對，再經由品質過濾。應用此框架，我們建構了多體裁合集（Multi-Genre Collection），一個包含5萬條示例的語料庫，涵蓋13種創意體裁，包括故事、饒舌、歌詞、劇本、遊戲設計、角色設計及其他創意格式。在分佈外寫作基準與保留體裁診斷上的實驗顯示，在我們數據上微調的模型不僅持續超越基礎模型與寫作專用基線，也勝過在現有寫作語料庫上訓練的模型。體裁數量的消融實驗進一步指出，受控的體裁擴展——而非僅以故事為中心的擴展——是穩健創意寫作能力的關鍵驅動因素。

English

High-quality creative writing data for large language models (LLMs) remains dominated by story-centric data, limiting models' ability to follow the structural and functional conventions of diverse creative formats. We propose an attribute-guided genre expansion framework for scaling creative writing data beyond story generation. By separating thematic breadth from genre-form control, our framework leverages human-authored story prompts as diverse creative seeds, while utilizing manually curated genre attributes to enforce distinct structural, stylistic, and formatting conventions. We combine these to prompt strong LLMs for genre-faithful query-response pairs, which are then quality-filtered. Applying this framework, we construct the Multi-Genre Collection, a 50K-example corpus spanning 13 creative genres, including story, rap, lyrics, scripts, game design, character design, and other creative formats. Experiments across out-of-distribution writing benchmarks and held-out genre diagnostics demonstrate that models fine-tuned on our data consistently surpass not only base models and writing-specialized baselines, but also models trained on existing writing corpora. Genre-count ablations further indicate that controlled genre expansion, rather than story-centric scaling alone, is a key driver of robust creative writing capability.