Chain-of-Dictionary Prompting Elicits Translation in Large Language
  Models
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.
645da298f5760d1530d6d899
Hongyuan Lu
645da298f5760d1530d6d89a
Haoyang Huang
admin_assigned
2023-06-02T13:07:27.498Z
63455a98ac1cb29fb2a55907
/avatars/1dfa775e2816725faf0133ecc7439c0a.svg
Huang Haoyang
user
Knighhy
645da298f5760d1530d6d89b
Dongdong Zhang
claimed_verified
2023-05-15T07:34:31.337Z
640809b4656adc27c7733ee0
/avatars/e4f075e4927dae7845d17d396addd3a2.svg
Dongdong Zhang
user
zddfunseeker
645da298f5760d1530d6d89c
Haoran Yang
645da298f5760d1530d6d89d
Wai Lam
645da298f5760d1530d6d89e
Furu Wei
admin_assigned
2023-06-02T13:07:59.634Z
6368c512fbfe97c16a40baba
/avatars/1c23bc7c0b6d9225699ce27647623d7a.svg
Furu Wei
user
thegenerality
Chain-of-Dictionary Prompting Elicits Translation in Large Language
  Models
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.
CoD enhances LLMs in MNMT by incorporating multilingual dictionaries, significantly improving translation performance, especially for low-resource languages.
large language models multilingual neural machine translation parallel data rare words low-resource languages in-context learning demonstration multilingual dictionaries CoD FLORES-200 ChrF++