---
title: "New Trends for Modern Machine Translation with — Liu 2026"
description: "This position paper from Alibaba Group and the University of Edinburgh argues that Large Reasoning Models (LRMs), which use Chain-of-Thought (CoT)…"
authors: ["Sinuo Liu", "Chenyang Lyu", "Minghao Wu", "Zifu Shang", "Longyue Wang", "Weihua Luo", "Kaifu Zhang"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.675/"
doi: "10.63317/24ab4svxeipr"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/liu-2026-new-trends-modern-machine-translation/"
---
[Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/liu-2026-new-trends-modern-machine-translation/)

[home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2026](https://md.research.elamur.ai/free/papers/2026/page/4/) / New Trends for Modern Machine Translation with Large Reasoning Models

# New Trends for Modern Machine Translation with Large Reasoning Models

**Authors:** Sinuo Liu, [Chenyang Lyu](https://md.research.elamur.ai/free/authors/chenyang-lyu/), Minghao Wu, Zifu Shang, [Longyue Wang](https://md.research.elamur.ai/free/authors/longyue-wang/), Weihua Luo, Kaifu Zhang · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.675/) · [PDF](https://aclanthology.org/2026.lrec-1.675.pdf) · [DOI 10.63317/24ab4svxeipr](https://doi.org/10.63317/24ab4svxeipr) · [OpenAlex](https://openalex.org/W4416039519)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14) · [16](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
This position paper from Alibaba Group and the University of Edinburgh argues that Large Reasoning Models (LRMs), which use Chain-of-Thought (CoT) reasoning, change machine translation (MT). In this view, translation is no longer a direct source-to-target mapping. It becomes a reasoning task that weighs context, culture and intent. The authors name three shifts. First, reasoning helps keep discourse coherent and resolve ambiguity. Second, models can infer audience and socio-linguistic norms. Third, models can catch and repair their own errors during inference, especially on noisy input. The authors support these claims with qualitative case studies in stylized, document-level and multimodal translation, using DeepSeek-R1 and other models. They do not report benchmark scores. They also describe new behaviours, such as automatic pivoting through a third language, and new risks, such as over-localization and slow inference. The paper matters because it reframes MT systems as multilingual reasoning agents and sets out a research agenda for evaluating and controlling them.

## Key points
- **Models compared:** In a Japanese haiku → Chinese test, the authors compared DeepSeek-R1, DeepSeek-V3, OpenAI-o3-mini and QwQ-32B-Preview.
  - When R1 was asked to analyse the style itself, it tended to produce Chinese-style verse and dropped the 5-7-5 structure.
  - When R1 was explicitly told to use haiku form, it usually kept 5-7-5.
- **Document-level case:** R1 translated a scientific abstract into Chinese with consistent terminology and a formal academic tone. Non-reasoning models overused "我们" for "we".
- **Auto-pivot translation:** LRMs route translation between two other languages through English or Chinese without being instructed to.
- **Open challenges:** The authors flag over-localization (output fits target norms more than the source's style) and inference efficiency, citing CoT compression work such as TokenSkip.
- **Publication:** LREC 2026 proceedings, pages 8556–8566. Related LRM translation work cited includes Marco-o1 and DRT-o1.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

Topics: [Machine translation](https://md.research.elamur.ai/free/topics/machine-translation/)

## Cited by

Cited by

16 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 15 not yet.

- [Beyond Simple Term Injection: Reasoning Models for Legal Translation in a Non-Dominant Language Variety](https://md.research.elamur.ai/free/papers/natale-2026-beyond-simple-term-injection-reasoning/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{liu2026new,
  title = {{New Trends for Modern Machine Translation with Large Reasoning Models}},
  author = {Liu, Sinuo and Lyu, Chenyang and Wu, Minghao and Shang, Zifu and Wang, Longyue and Luo, Weihua and Zhang, Kaifu},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/24ab4svxeipr},
  url = {https://doi.org/10.63317/24ab4svxeipr}
}
```

- [← previous New Encoders for German Trained from Scratch: Comparing ModernGBERT with Converted LLM2Vec Models](https://md.research.elamur.ai/free/papers/wunderle-2026-new-encoders-german-trained-scratch/)
- [next → NITI: Neural Plan Concretization for Incremental Execution, Bridging and Trigger Inference from Underspecified Human Policies](https://md.research.elamur.ai/free/papers/banerjee-2026-niti-neural-plan-concretization-incremental/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/papers/2026/page/4/)

## References

51 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al. 2023. A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023. [find](https://scholar.google.com/scholar?q=Yejin+Bang%2C+Samuel+Cahyawijaya%2C+Nayeon+Lee%2C+Wenliang+Dai%2C+Dan+Su%2C+Bryan+Wilie%2C+Holy+Lovenia%2C+Ziwei+Ji%2C+Tiezheng+Yu%2C+Willy+Chung%2C+et+al%2E+2023%2E+A+multitask%2C+multilingual%2C+multimodal+evaluation+of+chatgp) [our card](https://md.research.elamur.ai/free/papers/bang-2023-multitask-multilingual-multimodal-evaluation-chatgpt/)
2. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901. [find](https://scholar.google.com/scholar?q=Tom+Brown%2C+Benjamin+Mann%2C+Nick+Ryder%2C+Melanie+Subbiah%2C+Jared+D+Kaplan%2C+Prafulla+Dhariwal%2C+Arvind+Neelakantan%2C+Pranav+Shyam%2C+Girish+Sastry%2C+Amanda+Askell%2C+et+al%2E+2020%2E+Language+models+are+few%2Dshot+lear)
3. Sheila Castilho, Joss Moorkens, Federico Gaspari, Iacer Calixto, John Tinsley, and Andy Way. 2017. Is neural machine translation the new state of the art? The Prague Bulletin of Mathematical Linguistics. [find](https://scholar.google.com/scholar?q=Sheila+Castilho%2C+Joss+Moorkens%2C+Federico+Gaspari%2C+Iacer+Calixto%2C+John+Tinsley%2C+and+Andy+Way%2E+2017%2E+Is+neural+machine+translation+the+new+state+of+the+art%3F+The+Prague+Bulletin+of+Mathematical+Linguisti)
4. Andong Chen, Yuchen Song, Wenxin Zhu, Kehai Chen, Muyun Yang, Tiejun Zhao, et al. 2025. Evaluating o1-like llms: Unlocking reasoning for translation through comprehensive analysis. arXiv preprint arXiv:2502.11544. [find](https://scholar.google.com/scholar?q=Andong+Chen%2C+Yuchen+Song%2C+Wenxin+Zhu%2C+Kehai+Chen%2C+Muyun+Yang%2C+Tiejun+Zhao%2C+et+al%2E+2025%2E+Evaluating+o1%2Dlike+llms%3A+Unlocking+reasoning+for+translation+through+comprehensive+analysis%2E+arXiv+preprint+arXi)
5. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374. [find](https://scholar.google.com/scholar?q=Mark+Chen%2C+Jerry+Tworek%2C+Heewoo+Jun%2C+Qiming+Yuan%2C+Henrique+Ponde+de+Oliveira+Pinto%2C+Jared+Kaplan%2C+Harri+Edwards%2C+Yuri+Burda%2C+Nicholas+Joseph%2C+Greg+Brockman%2C+et+al%2E+2021%2E+Evaluating+large+language+mode) [our card](https://md.research.elamur.ai/free/papers/chen-2021-evaluating-large-language-models-trained/)
6. Raj Dabre, Aizhan Imankulova, Masahiro Kaneko, and Abhisek Chakrabarty. 2021. Simultaneous multi-pivot neural machine translation. [find](https://scholar.google.com/scholar?q=Raj+Dabre%2C+Aizhan+Imankulova%2C+Masahiro+Kaneko%2C+and+Abhisek+Chakrabarty%2E+2021%2E+Simultaneous+multi%2Dpivot+neural+machine+translation%2E)
7. DeepSeek-AI. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. [find](https://scholar.google.com/scholar?q=DeepSeek%2DAI%2E+2025%2E+Deepseek%2Dr1%3A+Incentivizing+reasoning+capability+in+llms+via+reinforcement+learning%2E) [our card](https://md.research.elamur.ai/free/papers/deepseek-2025-ai-deepseek-r1-incentivizing-reasoning-capability/)
8. Oluwaseyi Feyisetan, Sepideh Ghanavati, Patricia Thaine, Ivan Habernal, and Fatemehsadat Mireshghallah, editors. 2022. Proceedings of the Fourth Workshop on Privacy in Natural Language Processing. Association for Computational Linguistics, Seattle, United States. [find](https://scholar.google.com/scholar?q=Oluwaseyi+Feyisetan%2C+Sepideh+Ghanavati%2C+Patricia+Thaine%2C+Ivan+Habernal%2C+and+Fatemehsadat+Mireshghallah%2C+editors%2E+2022%2E+Proceedings+of+the+Fourth+Workshop+on+Privacy+in+Natural+Language+Processing%2E+Ass)
9. Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, Jindřich Helcl, and Alexandra Birch. 2022. Survey of low-resource machine translation. Computational Linguistics, 48(3):673–732. [find](https://scholar.google.com/scholar?q=Barry+Haddow%2C+Rachel+Bawden%2C+Antonio+Valerio+Miceli+Barone%2C+Jind%C5%99ich+Helcl%2C+and+Alexandra+Birch%2E+2022%2E+Survey+of+low%2Dresource+machine+translation%2E+Computational+Linguistics%2C+48%283%29%3A673%E2%80%93732%2E) [our card](https://md.research.elamur.ai/free/papers/haddow-2022-survey-low-resource-machine-translation/)
10. Muhammad Usman Hadi, Qasem Al-Tashi, Rizwan Qureshi, Abbas Shah, Amgad Muneer, Muhammad Irfan, Anas Zafar, Muhammad Bilal Shaikh, Naveed Akhtar, Mohammed Ali Al-Garadi, et al. 2023. Large language models: A comprehensive survey of its applications, challenges, limitations, and future prospects. [find](https://scholar.google.com/scholar?q=Muhammad+Usman+Hadi%2C+Qasem+Al%2DTashi%2C+Rizwan+Qureshi%2C+Abbas+Shah%2C+Amgad+Muneer%2C+Muhammad+Irfan%2C+Anas+Zafar%2C+Muhammad+Bilal+Shaikh%2C+Naveed+Akhtar%2C+Mohammed+Ali+Al%2DGaradi%2C+et+al%2E+2023%2E+Large+language+mod)
11. Jie He, Tao Wang, Deyi Xiong, and Qun Liu. 2020. The box is in the pen: Evaluating commonsense reasoning in neural machine translation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3662–3672, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Jie+He%2C+Tao+Wang%2C+Deyi+Xiong%2C+and+Qun+Liu%2E+2020%2E+The+box+is+in+the+pen%3A+Evaluating+commonsense+reasoning+in+neural+machine+translation%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EM)
12. Zhiwei He, Tian Liang, Wenxiang Jiao, Zhuosheng Zhang, Yujiu Yang, Rui Wang, Zhaopeng Tu, Shuming Shi, and Xing Wang. 2023. Exploring human-like translation strategy with large language models. arXiv preprint arXiv:2305.04118. [find](https://scholar.google.com/scholar?q=Zhiwei+He%2C+Tian+Liang%2C+Wenxiang+Jiao%2C+Zhuosheng+Zhang%2C+Yujiu+Yang%2C+Rui+Wang%2C+Zhaopeng+Tu%2C+Shuming+Shi%2C+and+Xing+Wang%2E+2023%2E+Exploring+human%2Dlike+translation+strategy+with+large+language+models%2E+arXiv+) [our card](https://md.research.elamur.ai/free/papers/he-2024-exploring-human-like-translation-strategy/)
13. Zhiwei He, Xing Wang, Wenxiang Jiao, Zhuosheng Zhang, Rui Wang, Shuming Shi, and Zhaopeng Tu. 2024. Improving machine translation with human feedback: An exploration of quality estimation as a reward model. arXiv preprint arXiv:2401.12873. [find](https://scholar.google.com/scholar?q=Zhiwei+He%2C+Xing+Wang%2C+Wenxiang+Jiao%2C+Zhuosheng+Zhang%2C+Rui+Wang%2C+Shuming+Shi%2C+and+Zhaopeng+Tu%2E+2024%2E+Improving+machine+translation+with+human+feedback%3A+An+exploration+of+quality+estimation+as+a+reward+)
14. Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. 2024. Gpt-4o system card. arXiv preprint arXiv:2410.21276. [find](https://scholar.google.com/scholar?q=Aaron+Hurst%2C+Adam+Lerer%2C+Adam+P+Goucher%2C+Adam+Perelman%2C+Aditya+Ramesh%2C+Aidan+Clark%2C+AJ+Ostrow%2C+Akila+Welihinda%2C+Alan+Hayes%2C+Alec+Radford%2C+et+al%2E+2024%2E+Gpt%2D4o+system+card%2E+arXiv+preprint+arXiv%3A2410%2E212) [our card](https://md.research.elamur.ai/free/papers/openai-2024-gpt-4o-system-card/)
15. Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. 2024. Openai o1 system card. arXiv preprint arXiv:2412.16720. [find](https://scholar.google.com/scholar?q=Aaron+Jaech%2C+Adam+Kalai%2C+Adam+Lerer%2C+Adam+Richardson%2C+Ahmed+El%2DKishky%2C+Aiden+Low%2C+Alec+Helyar%2C+Aleksander+Madry%2C+Alex+Beutel%2C+Alex+Carney%2C+et+al%2E+2024%2E+Openai+o1+system+card%2E+arXiv+preprint+arXiv%3A2412) [our card](https://md.research.elamur.ai/free/papers/openai-2024-openai-o1-system-card/)
16. Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, and Zhaopeng Tu. 2023. Is chatgpt a good translator? a preliminary study. arXiv preprint arXiv:2301.08745. [find](https://scholar.google.com/scholar?q=Wenxiang+Jiao%2C+Wenxuan+Wang%2C+Jen%2Dtse+Huang%2C+Xing+Wang%2C+and+Zhaopeng+Tu%2E+2023%2E+Is+chatgpt+a+good+translator%3F+a+preliminary+study%2E+arXiv+preprint+arXiv%3A2301%2E08745%2E) [our card](https://md.research.elamur.ai/free/papers/jiao-2023-chatgpt-good-translator-yes-gpt/)
17. Yunsu Kim, Petre Petrov, Pavel Petrushkov, Shahram Khadivi, and Hermann Ney. 2019. Pivot-based transfer learning for neural machine translation between non-English languages. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Con… [find](https://scholar.google.com/scholar?q=Yunsu+Kim%2C+Petre+Petrov%2C+Pavel+Petrushkov%2C+Shahram+Khadivi%2C+and+Hermann+Ney%2E+2019%2E+Pivot%2Dbased+transfer+learning+for+neural+machine+translation+between+non%2DEnglish+languages%2E+In+Proceedings+of+the+201)
18. Oleksandra Klymenko, Stephen Meisenbacher, and Florian Matthes. 2022. Differential privacy in natural language processing the story so far. In Proceedings of the Fourth Workshop on Privacy in Natural Language Processing, pages 1–11, Seattle, United States. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Oleksandra+Klymenko%2C+Stephen+Meisenbacher%2C+and+Florian+Matthes%2E+2022%2E+Differential+privacy+in+natural+language+processing+the+story+so+far%2E+In+Proceedings+of+the+Fourth+Workshop+on+Privacy+in+Natural+)
19. Tom Kocmi, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Thamme Gowda, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Rebecca Knowles, Philipp Koehn, Christof Monz, Makoto Morishita, Masaaki Nagata, Toshiaki Nakazawa, Michal Novák, Martin Popel, and Maja Popović.… [find](https://scholar.google.com/scholar?q=Tom+Kocmi%2C+Rachel+Bawden%2C+Ond%C5%99ej+Bojar%2C+Anton+Dvorkovich%2C+Christian+Federmann%2C+Mark+Fishel%2C+Thamme+Gowda%2C+Yvette+Graham%2C+Roman+Grundkiewicz%2C+Barry+Haddow%2C+Rebecca+Knowles%2C+Philipp+Koehn%2C+Christof+Monz)
20. Philipp Koehn and Rebecca Knowles. 2017. Six challenges for neural machine translation. arXiv preprint arXiv:1706.03872. [find](https://scholar.google.com/scholar?q=Philipp+Koehn+and+Rebecca+Knowles%2E+2017%2E+Six+challenges+for+neural+machine+translation%2E+arXiv+preprint+arXiv%3A1706%2E03872%2E)
21. Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, Md Amran Hossen Bhuiyan, Shafiq Joty, and Jimmy Huang. 2023. A systematic study and comprehensive evaluation of ChatGPT on benchmark datasets. In Findings of the Association for Computational Linguistics: ACL 2023, pages 431–469, Toronto, Canad… [find](https://scholar.google.com/scholar?q=Md+Tahmid+Rahman+Laskar%2C+M+Saiful+Bari%2C+Mizanur+Rahman%2C+Md+Amran+Hossen+Bhuiyan%2C+Shafiq+Joty%2C+and+Jimmy+Huang%2E+2023%2E+A+systematic+study+and+comprehensive+evaluation+of+ChatGPT+on+benchmark+datasets%2E+I)
22. Haonan Li, Fajri Koto, Minghao Wu, Alham Fikri Aji, and Timothy Baldwin. 2023a. Bactrianx: A multilingual replicable instruction-following model with low-rank adaptation. arXiv preprint arXiv:2305.15011. [find](https://scholar.google.com/scholar?q=Haonan+Li%2C+Fajri+Koto%2C+Minghao+Wu%2C+Alham+Fikri+Aji%2C+and+Timothy+Baldwin%2E+2023a%2E+Bactrianx%3A+A+multilingual+replicable+instruction%2Dfollowing+model+with+low%2Drank+adaptation%2E+arXiv+preprint+arXiv%3A2305%2E150)
23. Yansong Li, Zhixing Tan, and Yang Liu. 2023b. Privacy-preserving prompt tuning for large language model services. [find](https://scholar.google.com/scholar?q=Yansong+Li%2C+Zhixing+Tan%2C+and+Yang+Liu%2E+2023b%2E+Privacy%2Dpreserving+prompt+tuning+for+large+language+model+services%2E)
24. Chenyang Lyu, Zefeng Du, Jitao Xu, Yitao Duan, Minghao Wu, Teresa Lynn, Alham Fikri Aji, Derek F. Wong, and Longyue Wang. 2024. A paradigm shift: The future of machine translation lies with large language models. In Proceedings of the 2024 Joint International Conference on Computational Linguistics,… [find](https://scholar.google.com/scholar?q=Chenyang+Lyu%2C+Zefeng+Du%2C+Jitao+Xu%2C+Yitao+Duan%2C+Minghao+Wu%2C+Teresa+Lynn%2C+Alham+Fikri+Aji%2C+Derek+F%2E+Wong%2C+and+Longyue+Wang%2E+2024%2E+A+paradigm+shift%3A+The+future+of+machine+translation+lies+with+large+lang)
25. Yasmin Moslem, Rejwanul Haque, and Andy Way. 2023. Adaptive machine translation with large language models. arXiv preprint arXiv:2301.13294. [find](https://scholar.google.com/scholar?q=Yasmin+Moslem%2C+Rejwanul+Haque%2C+and+Andy+Way%2E+2023%2E+Adaptive+machine+translation+with+large+language+models%2E+arXiv+preprint+arXiv%3A2301%2E13294%2E) [our card](https://md.research.elamur.ai/free/papers/moslem-2023-adaptive-machine-translation-large-language/)
26. Xing Niu and Marine Carpuat. 2020. Controlling neural machine translation formality with synthetic supervision. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05):8568– 8575. [find](https://scholar.google.com/scholar?q=Xing+Niu+and+Marine+Carpuat%2E+2020%2E+Controlling+neural+machine+translation+formality+with+synthetic+supervision%2E+Proceedings+of+the+AAAI+Conference+on+Artificial+Intelligence%2C+34%2805%29%3A8568%E2%80%93+8575%2E)
27. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Gray, et al. 2022. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Long+Ouyang%2C+Jeffrey+Wu%2C+Xu+Jiang%2C+Diogo+Almeida%2C+Carroll+Wainwright%2C+Pamela+Mishkin%2C+Chong+Zhang%2C+Sandhini+Agarwal%2C+Katarina+Slama%2C+Alex+Gray%2C+et+al%2E+2022%2E+Training+language+models+to+follow+instruct)
28. Jianhui Pang, Fanghua Ye, Longyue Wang, Dian Yu, Derek F Wong, Shuming Shi, and Zhaopeng Tu. 2024. Salute the classic: Revisiting challenges of machine translation in the age of large language models. arXiv preprint arXiv:2401.08350. [find](https://scholar.google.com/scholar?q=Jianhui+Pang%2C+Fanghua+Ye%2C+Longyue+Wang%2C+Dian+Yu%2C+Derek+F+Wong%2C+Shuming+Shi%2C+and+Zhaopeng+Tu%2E+2024%2E+Salute+the+classic%3A+Revisiting+challenges+of+machine+translation+in+the+age+of+large+language+models%2E)
29. Qwen, :, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng… [find](https://scholar.google.com/scholar?q=Qwen%2C+%3A%2C+An+Yang%2C+Baosong+Yang%2C+Beichen+Zhang%2C+Binyuan+Hui%2C+Bo+Zheng%2C+Bowen+Yu%2C+Chengyuan+Li%2C+Dayiheng+Liu%2C+Fei+Huang%2C+Haoran+Wei%2C+Huan+Lin%2C+Jian+Yang%2C+Jianhong+Tu%2C+Jianwei+Zhang%2C+Jianxin+Yang%2C+Jiaxi+)
30. Nathaniel R Robinson, Perez Ogayo, David R Mortensen, and Graham Neubig. 2023. Chatgpt mt: Competitive for high-(but not low-) resource languages. arXiv preprint arXiv:2309.07423. [find](https://scholar.google.com/scholar?q=Nathaniel+R+Robinson%2C+Perez+Ogayo%2C+David+R+Mortensen%2C+and+Graham+Neubig%2E+2023%2E+Chatgpt+mt%3A+Competitive+for+high%2D%28but+not+low%2D%29+resource+languages%2E+arXiv+preprint+arXiv%3A2309%2E07423%2E) [our card](https://md.research.elamur.ai/free/papers/robinson-2023-chatgpt-mt-competitive-high-but/)
31. Satoshi Sato and Makoto Nagao. 1990. Toward memory-based translation. In 13th International Conference on Computational Linguistics, COL-ING 1990, University of Helsinki, Finland, August 20-25, 1990, pages 247–252. [find](https://scholar.google.com/scholar?q=Satoshi+Sato+and+Makoto+Nagao%2E+1990%2E+Toward+memory%2Dbased+translation%2E+In+13th+International+Conference+on+Computational+Linguistics%2C+COL%2DING+1990%2C+University+of+Helsinki%2C+Finland%2C+August+20%2D25%2C+1990%2C+)
32. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Controlling politeness in neural machine translation via side constraints. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 35–40, San Diego… [find](https://scholar.google.com/scholar?q=Rico+Sennrich%2C+Barry+Haddow%2C+and+Alexandra+Birch%2E+2016%2E+Controlling+politeness+in+neural+machine+translation+via+side+constraints%2E+In+Proceedings+of+the+2016+Conference+of+the+North+American+Chapter+o) [our card](https://md.research.elamur.ai/free/papers/sennrich-2016-controlling-politeness-neural-machine-translation/)
33. Felix Stahlberg. 2020. Neural machine translation: A review. Journal of Artificial Intelligence Research, 69:343–418. [find](https://scholar.google.com/scholar?q=Felix+Stahlberg%2E+2020%2E+Neural+machine+translation%3A+A+review%2E+Journal+of+Artificial+Intelligence+Research%2C+69%3A343%E2%80%93418%2E)
34. Umut Sulubacak, Ozan Caglayan, Stig-Arne Grönroos, Aku Rouhe, Desmond Elliott, Lucia Specia, and Jörg Tiedemann. 2020. Multimodal machine translation through visuals and speech. Machine Translation, 34:97–147. [find](https://scholar.google.com/scholar?q=Umut+Sulubacak%2C+Ozan+Caglayan%2C+Stig%2DArne+Gr%C3%B6nroos%2C+Aku+Rouhe%2C+Desmond+Elliott%2C+Lucia+Specia%2C+and+J%C3%B6rg+Tiedemann%2E+2020%2E+Multimodal+machine+translation+through+visuals+and+speech%2E+Machine+Translation%2C+3)
35. Qwen Team. 2024a. Qvq: To see the world with wisdom. [find](https://scholar.google.com/scholar?q=Qwen+Team%2E+2024a%2E+Qvq%3A+To+see+the+world+with+wisdom%2E)
36. Qwen Team. 2024b. Qwq: Reflect deeply on the boundaries of the unknown. [find](https://scholar.google.com/scholar?q=Qwen+Team%2E+2024b%2E+Qwq%3A+Reflect+deeply+on+the+boundaries+of+the+unknown%2E)
37. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. [find](https://scholar.google.com/scholar?q=Hugo+Touvron%2C+Thibaut+Lavril%2C+Gautier+Izacard%2C+Xavier+Martinet%2C+Marie%2DAnne+Lachaux%2C+Timoth%C3%A9e+Lacroix%2C+Baptiste+Rozi%C3%A8re%2C+Naman+Goyal%2C+Eric+Hambro%2C+Faisal+Azhar%2C+Aurelien+Rodriguez%2C+Armand+Joulin%2C+Edoua)
38. Jun’ichi Tsujii. 1986. Future directions of machine translation. In Coling 1986 Volume 1: The 11th International Conference on Computational Linguistics. [find](https://scholar.google.com/scholar?q=Jun%E2%80%99ichi+Tsujii%2E+1986%2E+Future+directions+of+machine+translation%2E+In+Coling+1986+Volume+1%3A+The+11th+International+Conference+on+Computational+Linguistics%2E)
39. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Proces… [find](https://scholar.google.com/scholar?q=Ashish+Vaswani%2C+Noam+Shazeer%2C+Niki+Parmar%2C+Jakob+Uszkoreit%2C+Llion+Jones%2C+Aidan+N+Gomez%2C+%C5%81+ukasz+Kaiser%2C+and+Illia+Polosukhin%2E+2017%2E+Attention+is+all+you+need%2E+In+I%2E+Guyon%2C+U%2E+V%2E+Luxburg%2C+S%2E+Bengio%2C+H%2E)
40. Jiaan Wang, Fandong Meng, Yunlong Liang, and Jie Zhou. 2024a. Drt-o1: Optimized deep reasoning translation via long chain-of-thought. arXiv preprint arXiv:2412.17498. — [arXiv:2412.17498](https://arxiv.org/abs/2412.17498) [find](https://scholar.google.com/scholar?q=Jiaan+Wang%2C+Fandong+Meng%2C+Yunlong+Liang%2C+and+Jie+Zhou%2E+2024a%2E+Drt%2Do1%3A+Optimized+deep+reasoning+translation+via+long+chain%2Dof%2Dthought%2E+arXiv+preprint+arXiv%3A2412%2E17498%2E+%E2%80%94+%5BarXiv%3A2412%2E17498%5D%28https%3A%2F%2Farxi)
41. Longyue Wang. 2019. Discourse-aware neural machine translation. Ph.D. thesis, Dublin City University. School of Computing. [find](https://scholar.google.com/scholar?q=Longyue+Wang%2E+2019%2E+Discourse%2Daware+neural+machine+translation%2E+Ph%2ED%2E+thesis%2C+Dublin+City+University%2E+School+of+Computing%2E)
42. Longyue Wang, Siyou Liu, Chenyang Lyu, Wenxiang Jiao, Xing Wang, Jiahao Xu, Zhaopeng Tu, Yan Gu, Weiyu Chen, Minghao Wu, et al. 2024b. Findings of the wmt 2024 shared task on discourse-level literary translation. arXiv preprint arXiv:2412.11732. [find](https://scholar.google.com/scholar?q=Longyue+Wang%2C+Siyou+Liu%2C+Chenyang+Lyu%2C+Wenxiang+Jiao%2C+Xing+Wang%2C+Jiahao+Xu%2C+Zhaopeng+Tu%2C+Yan+Gu%2C+Weiyu+Chen%2C+Minghao+Wu%2C+et+al%2E+2024b%2E+Findings+of+the+wmt+2024+shared+task+on+discourse%2Dlevel+literary+)
43. Longyue Wang, Chenyang Lyu, Tianbo Ji, Zhirui Zhang, Dian Yu, Shuming Shi, and Zhaopeng Tu. 2023. Document-level machine translation with large language models. arXiv preprint arXiv:2304.02210. [find](https://scholar.google.com/scholar?q=Longyue+Wang%2C+Chenyang+Lyu%2C+Tianbo+Ji%2C+Zhirui+Zhang%2C+Dian+Yu%2C+Shuming+Shi%2C+and+Zhaopeng+Tu%2E+2023%2E+Document%2Dlevel+machine+translation+with+large+language+models%2E+arXiv+preprint+arXiv%3A2304%2E02210%2E) [our card](https://md.research.elamur.ai/free/papers/wang-2023-document-level-machine-translation-large/)
44. Yifan Wang, Zewei Sun, Shanbo Cheng, Weiguo Zheng, and Mingxuan Wang. 2022. Controlling styles in neural machine translation with activation prompt. arXiv preprint arXiv:2212.08909. [find](https://scholar.google.com/scholar?q=Yifan+Wang%2C+Zewei+Sun%2C+Shanbo+Cheng%2C+Weiguo+Zheng%2C+and+Mingxuan+Wang%2E+2022%2E+Controlling+styles+in+neural+machine+translation+with+activation+prompt%2E+arXiv+preprint+arXiv%3A2212%2E08909%2E)
45. Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2022a. Finetuned language models are zero-shot learners. In International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Jason+Wei%2C+Maarten+Bosma%2C+Vincent+Zhao%2C+Kelvin+Guu%2C+Adams+Wei+Yu%2C+Brian+Lester%2C+Nan+Du%2C+Andrew+M+Dai%2C+and+Quoc+V+Le%2E+2022a%2E+Finetuned+language+models+are+zero%2Dshot+learners%2E+In+International+Conferenc)
46. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022b. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824–24837. [find](https://scholar.google.com/scholar?q=Jason+Wei%2C+Xuezhi+Wang%2C+Dale+Schuurmans%2C+Maarten+Bosma%2C+Fei+Xia%2C+Ed+Chi%2C+Quoc+V+Le%2C+Denny+Zhou%2C+et+al%2E+2022b%2E+Chain%2Dof%2Dthought+prompting+elicits+reasoning+in+large+language+models%2E+Advances+in+neural+) [our card](https://md.research.elamur.ai/free/papers/wei-2022-chain-thought-prompting-elicits-reasoning/)
47. Hua Wu and Haifeng Wang. 2007. Pivot language approach for phrase-based statistical machine translation. Machine Translation, 21:165–181. [find](https://scholar.google.com/scholar?q=Hua+Wu+and+Haifeng+Wang%2E+2007%2E+Pivot+language+approach+for+phrase%2Dbased+statistical+machine+translation%2E+Machine+Translation%2C+21%3A165%E2%80%93181%2E)
48. Heming Xia, Yongqi Li, Chak Tou Leong, Wenjie Wang, and Wenjie Li. 2025. Tokenskip: Controllable chain-of-thought compression in llms. [find](https://scholar.google.com/scholar?q=Heming+Xia%2C+Yongqi+Li%2C+Chak+Tou+Leong%2C+Wenjie+Wang%2C+and+Wenjie+Li%2E+2025%2E+Tokenskip%3A+Controllable+chain%2Dof%2Dthought+compression+in+llms%2E)
49. Shuoheng Yang, Yuxin Wang, and Xiaowen Chu. 2020. A survey of deep learning techniques for neural machine translation. [find](https://scholar.google.com/scholar?q=Shuoheng+Yang%2C+Yuxin+Wang%2C+and+Xiaowen+Chu%2E+2020%2E+A+survey+of+deep+learning+techniques+for+neural+machine+translation%2E)
50. Hongbin Zhang, Kehai Chen, Xuefeng Bai, Xiucheng Li, Yang Xiang, and Min Zhang. 2025. Exploring translation mechanism of large language models. [find](https://scholar.google.com/scholar?q=Hongbin+Zhang%2C+Kehai+Chen%2C+Xuefeng+Bai%2C+Xiucheng+Li%2C+Yang+Xiang%2C+and+Min+Zhang%2E+2025%2E+Exploring+translation+mechanism+of+large+language+models%2E)
51. Yu Zhao, Huifeng Yin, Bo Zeng, Hao Wang, Tianqi Shi, Chenyang Lyu, Longyue Wang, Weihua Luo, and Kaifu Zhang. 2024. Marco-o1: Towards open reasoning models for open-ended solutions. arXiv preprint arXiv:2411.14405. [find](https://scholar.google.com/scholar?q=Yu+Zhao%2C+Huifeng+Yin%2C+Bo+Zeng%2C+Hao+Wang%2C+Tianqi+Shi%2C+Chenyang+Lyu%2C+Longyue+Wang%2C+Weihua+Luo%2C+and+Kaifu+Zhang%2E+2024%2E+Marco%2Do1%3A+Towards+open+reasoning+models+for+open%2Dended+solutions%2E+arXiv+preprint+arX)