title: Chain-of-Dictionary Prompting Elicits Translation in Large Language
    Models
summary: Large language models (LLMs) have shown surprisingly good performance in
  multilingual neural machine translation (MNMT) even when trained without
  parallel data. Yet, despite the fact that the amount of training data is
  gigantic, they still struggle with translating rare words, particularly for
  low-resource languages. Even worse, it is usually unrealistic to retrieve
  relevant demonstrations for in-context learning with low-resource languages on
  LLMs, which restricts the practical use of LLMs for translation -- how should
  we mitigate this problem? To this end, we present a novel method, CoD, which
  augments LLMs with prior knowledge with the chains of multilingual dictionaries
  for a subset of input words to elicit translation abilities for LLMs. Extensive
  experiments indicate that augmenting ChatGPT with CoD elicits large gains by up
  to 13x ChrF++ points for MNMT (3.08 to 42.63 for English to Serbian written in
  Cyrillic script) on FLORES-200 full devtest set. We further demonstrate the
  importance of chaining the multilingual dictionaries, as well as the
  superiority of CoD to few-shot demonstration for low-resource languages.
paper_authors._id: 645da298f5760d1530d6d899
paper_authors.name: Hongyuan Lu
paper_authors._id: 645da298f5760d1530d6d89a
paper_authors.name: Haoyang Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T13:07:27.498Z
paper_authors._id: 63455a98ac1cb29fb2a55907
paper_authors.avatarUrl: /avatars/1dfa775e2816725faf0133ecc7439c0a.svg
paper_authors.fullname: Huang Haoyang
paper_authors.type: user
paper_authors.user: Knighhy
paper_authors._id: 645da298f5760d1530d6d89b
paper_authors.name: Dongdong Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-15T07:34:31.337Z
paper_authors._id: 640809b4656adc27c7733ee0
paper_authors.avatarUrl: /avatars/e4f075e4927dae7845d17d396addd3a2.svg
paper_authors.fullname: Dongdong Zhang
paper_authors.type: user
paper_authors.user: zddfunseeker
paper_authors._id: 645da298f5760d1530d6d89c
paper_authors.name: Haoran Yang
paper_authors._id: 645da298f5760d1530d6d89d
paper_authors.name: Wai Lam
paper_authors._id: 645da298f5760d1530d6d89e
paper_authors.name: Furu Wei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T13:07:59.634Z
paper_authors._id: 6368c512fbfe97c16a40baba
paper_authors.avatarUrl: /avatars/1c23bc7c0b6d9225699ce27647623d7a.svg
paper_authors.fullname: Furu Wei
paper_authors.type: user
paper_authors.user: thegenerality
paper_title: Chain-of-Dictionary Prompting Elicits Translation in Large Language
    Models
paper_summary: Large language models (LLMs) have shown surprisingly good performance in
  multilingual neural machine translation (MNMT) even when trained without
  parallel data. Yet, despite the fact that the amount of training data is
  gigantic, they still struggle with translating rare words, particularly for
  low-resource languages. Even worse, it is usually unrealistic to retrieve
  relevant demonstrations for in-context learning with low-resource languages on
  LLMs, which restricts the practical use of LLMs for translation -- how should
  we mitigate this problem? To this end, we present a novel method, CoD, which
  augments LLMs with prior knowledge with the chains of multilingual dictionaries
  for a subset of input words to elicit translation abilities for LLMs. Extensive
  experiments indicate that augmenting ChatGPT with CoD elicits large gains by up
  to 13x ChrF++ points for MNMT (3.08 to 42.63 for English to Serbian written in
  Cyrillic script) on FLORES-200 full devtest set. We further demonstrate the
  importance of chaining the multilingual dictionaries, as well as the
  superiority of CoD to few-shot demonstration for low-resource languages.
paper_ai_summary: CoD enhances LLMs in MNMT by incorporating multilingual dictionaries, significantly improving translation performance, especially for low-resource languages.
paper_ai_keywords: large language models multilingual neural machine translation parallel data rare words low-resource languages in-context learning demonstration multilingual dictionaries CoD FLORES-200 ChrF++