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Leveraging large language models for machine translation has demonstrated promising results.
Wikimatrix: Mining 135m parallel sentences in 1620 language pairs from wikipedia
Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong, and Francisco Guzmán. 2019 · 1907
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Multi30K: Multilingual English-German image descriptions
Desmond Elliott, Stella Frank, Khalil Sima’an, and Lucia Specia. 2016 · 2016
Earlier work this paper cites.
Findings of the second shared task on multimodal machine translation and multilingual image description
Desmond Elliott, Stella Frank, Loïc Barrault, Fethi Bougares, and Lucia Specia. 2017 · 2017
Earlier work this paper cites.
Findings of the third shared task on multimodal machine translation
Loïc Barrault, Fethi Bougares, Lucia Specia, Chiraag Lala, Desmond Elliott, and Stella Frank. 2018 · 2018
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
Earlier work this paper cites.
Steering large language models for machine translation with finetuning and in-context learning
Duarte Alves, Nuno Guerreiro, João Alves, José Pombal, Ricardo Rei, José de Souza, Pierre Colombo, and Andre Martins. 2023 · 2023
Earlier work this paper cites.
Investigating the translation performance of a large multilingual language model: the case of BLOOM
Rachel Bawden and François Yvon. 2023 · 2023
Earlier work this paper cites.
Improving translation faithfulness of large language models via augmenting instructions
Yijie Chen, Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, and Jie Zhou. 2023 · 2023
Cited alongside, same era.
Efficient and effective text encoding for chinese llama and alpaca
Yiming Cui, Ziqing Yang, and Xin Yao. 2023 · 2023
Cited alongside, same era.
The unreasonable effectiveness of few-shot learning for machine translation
Xavier Garcia, Yamini Bansal, Colin Cherry, George Foster, Maxim Krikun, Melvin Johnson, and Orhan Firat. 2023 · 2023
Cited alongside, same era.
Parrot: Translating during chat using large language models tuned with human translation and feedback
Wenxiang Jiao, Jen-tse Huang, Wenxuan Wang, Zhiwei He, Tian Liang, Xing Wang, Shuming Shi, and Zhaopeng Tu. 2023 · 2023
Cited alongside, same era.
Grounding language models to images for multimodal inputs and outputs
Jing Yu Koh, Ruslan Salakhutdinov, and Daniel Fried. 2023 · 2023
Towards making the most of ChatGPT for machine translation
Keqin Peng, Liang Ding, Qihuang Zhong, Li Shen, Xuebo Liu, Min Zhang, Yuanxin Ouyang, and Dacheng Tao. 2023 · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
Later among the works it cites.
Prompting PaLM for translation: Assessing strategies and performance
David Vilar, Markus Freitag, Colin Cherry, Jiaming Luo, Viresh Ratnakar, and George Foster. 2023 · 2023
Later among the works it cites.
A paradigm shift in machine translation: Boosting translation performance of large language models
Haoran Xu, Young Jin Kim, Amr Sharaf, and Hany Hassan Awadalla. 2023 · 2023
Later among the works it cites.
Enhanced visual instruction tuning for text-rich image understanding
Yanzhe Zhang, Ruiyi Zhang, Jiuxiang Gu, Yufan Zhou, Nedim Lipka, Diyi Yang, and Tong Sun. 2023c · 2023
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Cited alongside, same era.
Jiahuan Li, Hao Zhou, Shujian Huang, Shanbo Chen, and Jiajun Chen. 2023 · 2023
Cited alongside, same era.
A practical survey on zero-shot prompt design for in-context learning
Yinheng Li. 2023 · 2023
Cited alongside, same era.
New trends in machine translation using large language models: Case examples with chatgpt
Chenyang Lyu, Jitao Xu, and Longyue Wang. 2023 · 2023
Cited alongside, same era.
Transfer visual prompt generator across llms
Ao Zhang, Hao Fei, Yuan Yao, Wei Ji, Li Li, Zhiyuan Liu, and Tat-Seng Chua. 2023a
Cited in the paper.
Prompting large language model for machine translation: A case study
Biao Zhang, Barry Haddow, and Alexandra Birch. 2023b
Cited in the paper.
Later among the works it cites.
LLM augmented LLMs: Expanding capabilities through composition
Rachit Bansal, Bidisha Samanta, Siddharth Dalmia, Nitish Gupta, Sriram Ganapathy, Abhishek Bapna, Prateek Jain, and Partha Talukdar. 2024 · 2024
Closest in time.
Aligning translation-specific understanding to general understanding in large language models
Yichong Huang, Xiaocheng Feng, Baohang Li, Chengpeng Fu, Wenshuai Huo, Ting Liu, and Bing Qin. 2024 · 2024
Closest in time.