Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs

TL;DR

This paper systematically evaluates prompt translation strategies in multilingual LLMs across 35 languages and multiple tasks, providing practical guidelines for optimal pre-translation approaches based on language and task characteristics.

Contribution

It introduces a modular prompt framework and assesses various pre-translation strategies, offering systematic insights and guidelines for multilingual prompt design.

Findings

Translation quality significantly affects performance.

Similarity to English influences translation effectiveness.

Optimal strategies vary by language resource level and task.

Abstract

Despite advances in the multilingual capabilities of Large Language Models (LLMs) across diverse tasks, English remains the dominant language for LLM research and development. So, when working with a different language, this has led to the widespread practice of pre-translation, i.e., translating the task prompt into English before inference. Selective pre-translation, a more surgical approach, focuses on translating specific prompt components. However, its current use is sporagic and lacks a systematic research foundation. Consequently, the optimal pre-translation strategy for various multilingual settings and tasks remains unclear. In this work, we aim to uncover the optimal setup for pre-translation by systematically assessing its use. Specifically, we view the prompt as a modular entity, composed of four functional parts: instruction, context, examples, and output, either of which could be translated or not. We evaluate pre-translation strategies across 35 languages covering both low and high-resource languages, on various tasks including Question Answering (QA), Natural Language Inference (NLI), Named Entity Recognition (NER), and Abstractive Summarization. Our experiments show the impact of factors as similarity to English, translation quality and the size of pre-trained data, on the model performance with pre-translation. We suggest practical guidelines for choosing optimal strategies in various multilingual settings.