@inproceedings{wang-etal-2026-seeing-poem,
title = "Seeing the Poem: Image-Semantic Detection of {AI}-Generated {M}odern {C}hinese Poetry with {MLLM}s",
author = "Wang, Shanshan and
Ye, Fengying and
Lyu, Hanjia and
Gou, Caiwen and
Wu, Junchao and
Yao, Jingming and
Xu, Cheng-zhong and
Luo, Jiebo and
Wong, Derek F.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.586/",
pages = "12371--12387",
ISBN = "979-8-89176-462-0",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Seeing the Poem: Image-Semantic Detection of AI-Generated Modern Chinese Poetry with MLLMs
%A Wang, Shanshan
%A Ye, Fengying
%A Lyu, Hanjia
%A Gou, Caiwen
%A Wu, Junchao
%A Yao, Jingming
%A Xu, Cheng-zhong
%A Luo, Jiebo
%A Wong, Derek F.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-seeing-poem
%X 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.
%U https://aclanthology.org/2026.findings-emnlp.586/
%P 12371-12387
Markdown (Informal)
[Seeing the Poem: Image-Semantic Detection of AI-Generated Modern Chinese Poetry with MLLMs](https://aclanthology.org/2026.findings-emnlp.586/) (Wang et al., Findings 2026)
ACL
- Shanshan Wang, Fengying Ye, Hanjia Lyu, Caiwen Gou, Junchao Wu, Jingming Yao, Cheng-zhong Xu, Jiebo Luo, and Derek F. Wong. 2026. Seeing the Poem: Image-Semantic Detection of AI-Generated Modern Chinese Poetry with MLLMs. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 12371–12387, Budapest, Hungary. Association for Computational Linguistics.