COIG-Writer: A High-Quality Dataset for Chinese Creative Writing with
  Thought Processes
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.
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Yunwen Li
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Shuangshuang Ying
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Xingwei Qu
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Xin Li
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XinLi
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XINLI1997
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Sheng Jin
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Minghao Liu
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minghao
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Liam-Liu
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Zhoufutu Wen
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Tianyu Zheng
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Xeron Du
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Qiguang Chen
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Qiguang Chen
user
LightChen2333
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Jiajun Shi
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Wangchunshu Zhou
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Jiazhan Feng
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Wanjun Zhong
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Libo Qin
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Stephen Huang
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Wanxiang Che
68f1d66a6e0bef323a68ffcf
Chenghua Lin
68f1d66a6e0bef323a68ffd0
Eli Zhang
COIG-Writer: A High-Quality Dataset for Chinese Creative Writing with
  Thought Processes
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.
COIG-Writer, a Chinese creative writing dataset, reveals that process supervision and general-purpose data are crucial for creative writing, with cultural-bound capabilities and lexical diversity impacting performance.
COIG-Writer creative writing dataset reverse-engineered prompt creative reasoning narrative logic linguistic expression process supervision general-purpose data cultural-bound capabilities cross-lingual transfer lexical diversity TTR paradox