Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.22654 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918516655915008 |
|---|---|
| author | Wang, Shanshan Ye, Fengying Lyu, Hanjia Gou, Caiwen Wu, Junchao Yao, Jingming Xu, Chengzhong Luo, Jiebo Wong, Derek F. |
| author_facet | Wang, Shanshan Ye, Fengying Lyu, Hanjia Gou, Caiwen Wu, Junchao Yao, Jingming Xu, Chengzhong Luo, Jiebo Wong, Derek F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22654 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Seeing the Poem: Image-Semantic Detection of AI-Generated Modern Chinese Poetry with MLLMs Wang, Shanshan Ye, Fengying Lyu, Hanjia Gou, Caiwen Wu, Junchao Yao, Jingming Xu, Chengzhong Luo, Jiebo Wong, Derek F. Computation and Language Computer Vision and Pattern Recognition 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. |
| title | Seeing the Poem: Image-Semantic Detection of AI-Generated Modern Chinese Poetry with MLLMs |
| topic | Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.22654 |