@inproceedings{guo-etal-2024-teaching,
    title = "Teaching Large Language Models to Translate on Low-resource Languages with Textbook Prompting",
    author = "Guo, Ping  and
      Ren, Yubing  and
      Hu, Yue  and
      Li, Yunpeng  and
      Zhang, Jiarui  and
      Zhang, Xingsheng  and
      Huang, Heyan",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1362",
    pages = "15685--15697",
    abstract = "Large Language Models (LLMs) have achieved impressive results in Machine Translation by simply following instructions, even without training on parallel data. However, LLMs still face challenges on low-resource languages due to the lack of pre-training data. In real-world situations, humans can become proficient in their native languages through abundant and meaningful social interactions and can also learn foreign languages effectively using well-organized textbooks. Drawing inspiration from human learning patterns, we introduce the Translate After LEarNing Textbook (TALENT) approach, which aims to enhance LLMs{'} ability to translate low-resource languages by learning from a textbook. TALENT follows a step-by-step process: (1) Creating a Textbook for low-resource languages. (2) Guiding LLMs to absorb the Textbook{'}s content for Syntax Patterns. (3) Enhancing translation by utilizing the Textbook and Syntax Patterns. We thoroughly assess TALENT{'}s performance using 112 low-resource languages from FLORES-200 with two LLMs: ChatGPT and BLOOMZ. Evaluation across three different metrics reveals that TALENT consistently enhances translation performance by 14.8{\%} compared to zero-shot baselines. Further analysis demonstrates that TALENT not only improves LLMs{'} comprehension of low-resource languages but also equips them with the knowledge needed to generate accurate and fluent sentences in these languages.",
}

Large Language Models (LLMs) have achieved impressive results in Machine Translation by simply following instructions, even without training on parallel data. However, LLMs still face challenges on low-resource languages due to the lack of pre-training data. In real-world situations, humans can become proficient in their native languages through abundant and meaningful social interactions and can also learn foreign languages effectively using well-organized textbooks. Drawing inspiration from human learning patterns, we introduce the Translate After LEarNing Textbook (TALENT) approach, which aims to enhance LLMs{'} ability to translate low-resource languages by learning from a textbook. TALENT follows a step-by-step process: (1) Creating a Textbook for low-resource languages. (2) Guiding LLMs to absorb the Textbook{'}s content for Syntax Patterns. (3) Enhancing translation by utilizing the Textbook and Syntax Patterns. We thoroughly assess TALENT{'}s performance using 112 low-resource languages from FLORES-200 with two LLMs: ChatGPT and BLOOMZ. Evaluation across three different metrics reveals that TALENT consistently enhances translation performance by 14.8{\%} compared to zero-shot baselines. Further analysis demonstrates that TALENT not only improves LLMs{'} comprehension of low-resource languages but also equips them with the knowledge needed to generate accurate and fluent sentences in these languages.
Guo, Ping Ren, Yubing Hu, Yue Li, Yunpeng Zhang, Jiarui Zhang, Xingsheng Huang, Heyan
Teaching Large Language Models to Translate on Low-resource Languages with Textbook Prompting