COIG-Writer：一个带有思维过程的高质量中文创意写作数据集
COIG-Writer: A High-Quality Dataset for Chinese Creative Writing with Thought Processes
AI总结：
针对大模型中文创意写作缺陷，提出COIG-Writer数据集，含1665个带思维过程的三元组，揭示过程监督需与通用数据配比及文化约束等关键洞见。
摘要：
大型语言模型在创意写作方面表现出系统性缺陷，尤其是在非英语语境中，因为训练数据稀缺且缺乏过程级监督。我们提出了COIG-Writer，这是一个新颖的中文创意写作数据集，通过对高质量文本的系统性逆向工程，同时捕捉多样化的输出及其背后的思维过程。与仅提供输入-输出对的现有数据集不同，COIG-Writer包含1,665个精心策划的三元组，涵盖51种体裁，每个三元组包含：（1）逆向工程的提示词，（2）记录决策过程的详细创意推理，以及（3）最终文本。通过全面实验，我们识别出创意写作的双组件模型：叙事逻辑（由过程监督提供）和语言表达（由通用数据维持）。我们的发现揭示了三个关键洞见：（1）过程监督高度有效，但需要通用数据的稳定化。至少需要一个创意样本与十二个通用样本的比例才能达到最佳性能；低于此阈值，胜率会逐渐下降（从62.75%降至35.78%）。（2）创意能力受文化约束，不存在跨语言迁移（中英文性能差距为89.26个百分点）。（3）词汇多样性与创意质量呈负相关（TTR悖论），表明高多样性是逻辑缺陷的补偿行为。这些发现确立了创意卓越源于逻辑支架与语言基础之间的相互作用，类似于数学推理在基础模型中增强但不能替代语言能力。
英文摘要：
Large language models exhibit systematic deficiencies in creative writing, particularly in non-English contexts where training data is scarce and lacks process-level supervision. We present COIG-Writer, a novel Chinese creative writing dataset that captures both diverse outputs and their underlying thought processes through systematic reverse-engineering of high-quality texts. Unlike existing datasets that provide only input-output pairs, COIG-Writer comprises 1,665 meticulously curated triplets spanning 51 genres, each containing: (1) a reverse-engineered prompt, (2) detailed creative reasoning documenting decision-making processes, and (3) the final text. Through comprehensive experiments, we identify a two-component model of creative writing: narrative logic (provided by process supervision) and linguistic expression (maintained by general-purpose data). Our findings reveal three critical insights: (1) Process supervision is highly effective but requires stabilization with general data. A ratio of at least one creative sample to twelve general samples is needed to achieve optimal performance; below this threshold, the win rate progressively degrades (from 62.75% down to 35.78%)., (2) creative capabilities are culturally-bound with no cross-lingual transfer (89.26pp gap between Chinese and English performance), and (3) lexical diversity inversely correlates with creative quality (TTR paradox), suggesting high diversity signals compensatory behavior for logical deficiencies. These findings establish that creative excellence emerges from the interaction between logical scaffolding and linguistic grounding, analogous to how mathematical reasoning enhances but cannot replace linguistic competence in foundation models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
