发表机构
Chung-Ang University（中央大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出五个可解释的韩语诗歌形式级特征，构建的逻辑回归分类器检测LLM生成韩语诗歌的AUC达83.60，且该特征可引导LLM生成更符合人类形式的诗歌。
AI 中文摘要
大语言模型（LLMs）在创作现代韩语诗歌时常常遇到困难，其生成的输出类似“断行散文”。我们解决两个关联任务：一是检测韩语诗歌是人类创作还是LLM生成，二是引导LLM生成在形式上更接近人类写作的诗歌。我们在四个形式级语言维度上量化了人类与LLM的差距：输出长度（Volume）、行末形式的多样性与连接性使用（Structure Variation）、行长度的不规则性（Rhythmic Irregularity）以及对标准正字法的遵守程度（Normative Adherence）。我们将这些维度转化为五个可解释特征。在检测任务中，基于这五个特征的逻辑回归分类器，在对七个未见过的LLM进行零样本分布外检测时，平均AUC-ROC达到83.60，而对比中最强的基准模型KatFishNet仅为75.84，绝对AUC提升7.76个点，相对提升10.23%；我们的分类体系之外存在一种特定于生成器的标点模式，仍属于边界案例。在生成任务中，对GPT-5.2的专家评估显示，特征引导生成的诗歌优于无约束基准，对GPT-5.2和Gemini-3的分析表明，目标长度、节奏和结尾统计量向人类分布靠拢。这些结果表明，可解释的、特定于语言的特征可打通LLM生成诗歌的诊断与引导环节。
英文摘要
LLMs often struggle with modern Korean poetry, producing outputs that resemble "line-broken prose." We address two coupled tasks: detecting whether a Korean poem is human- or LLM-authored, and guiding LLMs to generate poetry closer in form to human writing. We quantify the human-LLM gap along four form-level linguistic dimensions: output length (Volume), the diversity and connective use of line-final forms (Structure Variation), the irregularity of line lengths (Rhythmic Irregularity), and adherence to standard orthography (Normative Adherence). We operationalize these dimensions as five interpretable features. For detection, a logistic regression classifier over these five features attains an average AUC-ROC of 83.60 in zero-shot out-of-distribution detection across seven unseen LLMs, versus 75.84 for the strongest baseline in our comparison, KatFishNet, an absolute gain of 7.76 AUC points and a 10.23% relative improvement; one generator-specific punctuation pattern outside our taxonomy remains a boundary case. For generation, expert evaluation on GPT-5.2 prefers feature-guided poems over the unconstrained baseline, and analyses across GPT-5.2 and Gemini-3 show that targeted length, rhythm, and ending statistics move toward the human distribution. These results suggest that interpretable, language-specific features can bridge the diagnosis and guidance of LLM-generated poetry.
CommentsAccepted to Findings of EMNLP 2026
