Chain-of-Dictionary Prompting Elicits Translation in Large Language Models

TL;DR

This paper introduces CoD, a novel method that enhances large language models' translation capabilities for low-resource languages by chaining multilingual dictionaries, significantly improving translation quality especially where data is scarce.

Contribution

The paper proposes CoD, a new approach that uses chains of multilingual dictionaries to improve LLM translation performance for low-resource languages, outperforming few-shot learning methods.

Findings

CoD improves translation quality by up to 13x chrF++ points.

Chaining multilingual dictionaries is crucial for effectiveness.

CoD outperforms few-shot demonstration methods.

Abstract

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.