用图像语义辅助检测AI写的现代中文诗，效果超越纯文本方法。
Seeing the Poem: Image-Semantic Detection of AI-Generated Modern Chinese Poetry with MLLMs
- 结合诗歌内容的图像与文本，互补判断真伪。
- 新方法使LLM检测器宏平均F1达85.65%，达到当前最优。
- 适合关注AI内容检测、多模态分析的研究者。
以往检测研究显示大语言模型（LLM）无法有效作为检测器，但这些研究未涉及现代中文诗，且尚未有相关工作探索LLM在该任务上的表现。本文评估并提升了LLM作为现代中文诗检测器的性能，提出一种图像-语义引导的诗歌检测方法。相比传统方法，该方法创新性地引入反映诗歌内容的图像，通过示例驱动方式融合图像中的意义、意象与情感信息，与文本形成互补判断。实验结果表明，基于本方法的LLM检测器优于纯文本基线检测器，甚至超过最佳传统检测器RoBERTa。使用本方法的Gemini检测器达到85.65%的宏平均F1分数，达到当前最优水平。不同LLM在多种生成数据上的性能提升，验证了该方法的有效性。
原文摘要 · Abstract (English)
Previous detection studies have shown that LLMs cannot be effectively used as detectors, but these studies have not addressed modern Chinese poetry. Moreover, no relevant research has explored the performance of LLMs in detecting modern Chinese poetry. This paper evaluates and enhances the performance of LLMs as detectors for modern Chinese poetry, and proposes an image-semantic guided poetry detection method. Compared with traditional detection approaches, our method innovatively incorporates images that reflect the content of the poetry. Through example-driven approaches, our method effectively integrates information such as meaning, imagery, and feeling from the image, then forms a complementary judgment with the poem text. Experimental results demonstrate that the LLM detectors based on our method outperform baseline detectors based on plain text, and even surpass the best-performing traditional detector, RoBERTa. The Gemini detector using our method achieves a Macro-F1 score of 85.65%, reaching the state-of-the-art level. The performance improvements of different LLM detectors on multiple LLMs-generated data prove the effectiveness of our method.
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