Challenge: Traditional systems in this field usually accept keywords as user inputs, resulting in limited control over content.
Approach: They propose a Chinese classical poetry generation system based on token-free LLMs that allow unrestricted user instructions to be used.
Outcome: The proposed system outperforms traditional systems including Jiuge and GPT-4 in format accuracy and content quality.

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Yu Sheng: Human-in-Loop Classical Chinese Poetry Generation System (2023.eacl-demo)

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Challenge: Existing systems for poetry generation are not flexible in polishing and customization.
Approach: They propose a web-based poetry generation system that provides customization options for users with different backgrounds to engage in the process of poetry composition.
Outcome: The proposed system can generate and polish classical Chinese poetry compared to other vanilla models.
Who Wrote This Line? Evaluating the Detection of LLM-Generated Classical Chinese Poetry (2026.acl-long)

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Challenge: a recent study shows that large language models can generate text, but they can also fabricate large amounts of false or misleading content.
Approach: They propose a benchmark to detect LLM-generated classical Chinese poetry . they compare 12 different AI detectors to find out whether a poem is authored by AI .
Outcome: The proposed benchmark compared 12 AI detectors with a dataset of 30,664 Chinese poems . the results highlight the limitations of current Chinese text detectors .
Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System (P19-3)

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Challenge: Existing systems for automatic poetry generation are model-oriented, resulting in poor user participation.
Approach: They propose a human-machine collaborative Chinese classical poetry generation system called Jiuge . Jiuge allows users to revise unsatisfied parts of a generated poem draft repeatedly .
Outcome: The proposed system allows users to revise unsatisfied parts of a generated poem draft repeatedly.
Generating Major Types of Chinese Classical Poetry in a Uniformed Framework (2020.lrec-1)

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Challenge: Chinese classical poetry is one of the most valuable literary and cultural heritages of China . it has many particular characteristics in its language structure, ranging from form, sound to meaning . a proposed uniformed framework for generating major types of Chinese classical poems is proposed .
Approach: They propose a uniformed framework for generating major types of Chinese classical poems . they use a form- stressed weighting method to strengthen the control to the form of the generated poems a proposed framework is incorporated into Jiuge, the most influential Chinese classical poetry generation system developed by Tsinghua University.
Outcome: The proposed framework can generate Chinese classical poems of major types with high quality in form and content.
ByGPT5: End-to-End Style-conditioned Poetry Generation with Token-free Language Models (2023.acl-long)

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Challenge: End-to-end models learn to complete a task by directly learning all steps, without intermediary algorithms such as hand-crafted rules or post-processing.
Approach: They propose to train end-to-end poetry generation conditioned on styles such as rhyme, meter, and alliteration . they pre-train ByGPT5, a new token-free decoder-only language model, and fine-tune it on a custom corpus of English and German quatrains .
Outcome: The proposed model outperforms other models on a large custom corpus of English and German quatrains while being more parameter efficient and performing favorably compared to humans.
Capabilities and Evaluation Biases of Large Language Models in Classical Chinese Poetry Generation: A Case Study on Tang Poetry (2026.findings-acl)

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Challenge: Large Language Models (LLMs) are increasingly applied to creative domains, yet performance in classical Chinese poetry generation and evaluation remains poorly understood.
Approach: They propose a framework that combines computational metrics, LLM-as-a-judge assessment, and human expert validation to evaluate large language models.
Outcome: The proposed framework evaluates state-of-the-art LLMs across multiple dimensions of poetic quality in Tang poetry generation.
Automatic Poetry Generation from Prosaic Text (2020.acl-main)

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Challenge: In recent years, successful approaches have emerged to accurately model various aspects of natural language.
Approach: They propose to combine neural networks with a poetry generation system that only uses standard text as input . they use standard text to model syntactic well-formedness and topical coherence .
Outcome: The proposed framework is applied to the generation of poems in English and French . it uses standard, non-poetic text and its output is constrained to confer a poetic character .
Lingxi: A Diversity-aware Chinese Modern Poetry Generation System (2023.acl-demo)

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Challenge: Chinese modern poetry generation is a challenging task because of the word segmentation problem and decoding methods . the decoding method may induce repetition and boredom and lower the diversity of generated poetry.
Approach: They propose a Chinese word segmentation-based decoding system that incorporates Chinese word segments into tokenization.
Outcome: The proposed system can achieve high vocabulary coverage rate with a reasonable vocabulary size.
Generating Classical Chinese Poems from Vernacular Chinese (D19-1)

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Challenge: Existing models for classical Chinese poetry generation only allow users to use keywords to interfere with the meaning of generated poems.
Approach: They propose a model to generate classical Chinese poems from vernacular . their model uses unsupervised machine translation to generate Chinese poems . human evaluation shows it can generate high-quality poems comparable to amateur poems - authors .
Outcome: The proposed model improves the perplexity and BLEU of the proposed model compared with typical models and human evaluation shows it generates high-quality poems comparable to amateur poems.
SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition (2025.acl-long)

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Challenge: Creating lyrics and melodies in symbolic format requires expert knowledge of melody and an advanced understanding of lyrics.
Approach: They introduce SongComposer, a music-specialized large language model that can create symbolic lyrics and melodies following instructions.
Outcome: The proposed model outperforms existing models in symbolic song composition tasks.

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