# What is the Best Way for ChatGPT to Translate Poetry?

arXiv: https://arxiv.org/abs/2406.03450 · HTML: https://mlsanity.com/paper/2406.03450/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Why We Need New Evaluation Metrics for NLG](https://mlsanity.com/paper/1707.06875/) (2017)
- [Addressing the Rare Word Problem in Neural Machine Translation](https://mlsanity.com/paper/1410.8206/) (2014)
- [Incorporating Discrete Translation Lexicons into Neural Machine Translation](https://mlsanity.com/paper/1606.02006/) (2016)
- [How to Design Translation Prompts for ChatGPT: An Empirical Study](https://mlsanity.com/paper/2304.02182/) (2023)
- [Generating Chinese Classical Poems with RNN Encoder-Decoder](https://mlsanity.com/paper/1604.01537/) (2016)
- [Don't Go Far Off: An Empirical Study on Neural Poetry Translation](https://mlsanity.com/paper/2109.02972/) (2021)
- [Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model](https://mlsanity.com/paper/2404.16766/) (2024)
- [Prose2Poem: The Blessing of Transformers in Translating Prose to Persian Poetry](https://mlsanity.com/paper/2109.14934/) (2021)
- [FOCUS: Forging Originality through Contrastive Use in Self-Plagiarism for Language Models](https://mlsanity.com/paper/2406.00839/) (2024)

## Built on by (most cited first, 14 of 14)

- [Benchmarking the Detection of LLMs-Generated Modern Chinese Poetry](https://mlsanity.com/paper/2509.01620/) (2025)
- [Probing Semantic Alignment, Lexical Invariance, and Syntactic Influence in LLM Metaphor Processing](https://mlsanity.com/paper/2510.04120/) (2025)
- [Preconditioned Test-Time Adaptation for Out-of-Distribution Debiasing in Narrative Generation](https://mlsanity.com/paper/2603.13683/) (2026)
- [Can ChatGPT Really Understand Modern Chinese Poetry?](https://mlsanity.com/paper/2603.20851/) (2026)
- [Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language](https://mlsanity.com/paper/2604.15841/) (2026)
- [Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model](https://mlsanity.com/paper/2502.01090/) (2025)
- [MetricalARGS: A Taxonomy for Studying Metrical Poetry with LLMs](https://mlsanity.com/paper/2510.08188/) (2025)
- [Who Wrote This Line? Evaluating the Detection of LLM-Generated Classical Chinese Poetry](https://mlsanity.com/paper/2604.10101/) (2026)
- [Seeing the Poem: Image-Semantic Detection of AI-Generated Modern Chinese Poetry with MLLMs](https://mlsanity.com/paper/2605.22654/) (2026)
- [A Heuristic Perspective on Debiasing Language Models](https://mlsanity.com/paper/2608.00622/) (2026)
- [Translating Classical Poetry into Modern Prose](https://mlsanity.com/paper/2606.02806/) (2026)
- [Poller: Are LLMs Suitable for Evaluating the Poetry Understanding Task?](https://mlsanity.com/paper/2606.30556/) (2026)
- [Do Large Language Models Perform Well on Comprehending Poetic Logic in Modern Chinese Poetry?](https://mlsanity.com/paper/2608.21827/) (2026)
- [Acmite: Mitigating Gender Bias in LLMs through Concept-Guided Mutual Information](https://mlsanity.com/paper/2610.01696/) (2026)

## Abstract (arXiv)

Machine translation (MT) has historically faced significant challenges when applied to literary works, particularly in the domain of poetry translation. The advent of Large Language Models such as ChatGPT holds potential for innovation in this field. This study examines ChatGPT's capabilities in English-Chinese poetry translation tasks, utilizing targeted prompts and small sample scenarios to ascertain optimal performance. Despite promising outcomes, our analysis reveals persistent issues in the translations generated by ChatGPT that warrant attention. To address these shortcomings, we propose an Explanation-Assisted Poetry Machine Translation (EAPMT) method, which leverages monolingual poetry explanation as a guiding information for the translation process. Furthermore, we refine existing evaluation criteria to better suit the nuances of modern poetry translation. We engaged a panel of professional poets for assessments, complemented evaluations by using GPT-4. The results from both human and machine evaluations demonstrate that our EAPMT method outperforms traditional translation methods of ChatGPT and the existing online systems. This paper validates the efficacy of our method and contributes a novel perspective to machine-assisted literary translation.