Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis

TL;DR

This paper systematically evaluates large language models like GPT-4 for multilingual machine translation, revealing their strengths, limitations, and unique working patterns across various languages and resource settings.

Contribution

It provides a comprehensive empirical analysis of LLMs' translation performance, highlighting new insights into their resource efficiency and exemplar usage for low-resource languages.

Findings

GPT-4 outperforms NLLB in 40.91% of directions

LLMs can generate moderate translation for zero-resource languages

Cross-lingual exemplars improve low-resource translation

Abstract

Large language models (LLMs) have demonstrated remarkable potential in handling multilingual machine translation (MMT). In this paper, we systematically investigate the advantages and challenges of LLMs for MMT by answering two questions: 1) How well do LLMs perform in translating massive languages? 2) Which factors affect LLMs' performance in translation? We thoroughly evaluate eight popular LLMs, including ChatGPT and GPT-4. Our empirical results show that translation capabilities of LLMs are continually involving. GPT-4 has beat the strong supervised baseline NLLB in 40.91% of translation directions but still faces a large gap towards the commercial translation system like Google Translate, especially on low-resource languages. Through further analysis, we discover that LLMs exhibit new working patterns when used for MMT. First, LLM can acquire translation ability in a resource-efficient way and generate moderate translation even on zero-resource languages. Second, instruction semantics can surprisingly be ignored when given in-context exemplars. Third, cross-lingual exemplars can provide better task guidance for low-resource translation than exemplars in the same language pairs. Code will be released at: https://github.com/NJUNLP/MMT-LLM.