Who Wrote This Line? Evaluating the Detection of LLM-Generated Classical Chinese Poetry
Jiang Li, Tian Lan, Shanshan Wang, Zdongxing, Dianqing Lin, Guanglai Gao, Derek F. Wong, Xiangdong Su
Abstract
The rapid development of large language models (LLMs) has extended text generation tasks into the literary domain. However, AIgenerated literary creations has raised increasingly prominent issues of creative authenticity and ethics in literary world, making the detection of LLM-generated literary texts essential and urgent. While previous works have made significant progress in detecting AI-generated text, it has yet to address classical Chinese poetry. Due to the unique linguistic features of classical Chinese poetry, such as strict metrical regularity, a shared system of poetic imagery, and flexible syntax, distinguishing whether a poem is authored by AI presents a substantial challenge. To address these issues, we introduce ChangAn, a benchmark for detecting LLM-generated classical Chinese poetry that containing total 30,664 poems, 10,276 are human-written poems and 20,388 poems are generated by four popular LLMs. Based on ChangAn, we conducted a systematic evaluation of 12 AI detectors, investigating their performance variations across different text granularities and generation strategies. Our findings highlight the limitations of current Chinese text detectors, which fail to serve as reliable tools for detecting LLM-generated classical Chinese poetry. These results validate the effectiveness and necessity of our proposed ChangAn benchmark. Our dataset and code are available at https://github.com/VelikayaScarlet/ ChangAn . 1 AI诗歌成为获奖"钉子户","反AI诗歌联盟群"群主 希望相关部门对AI作品投稿尽快出台相关政策 2 《诗刊》副主编对AI诗歌投稿发出警告 "谁在写"引 发文学圈深度探讨|封面头条 3 用AI诗作投稿:不是走捷径,而是绕远路
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