Aligning Large Language Models to Low-Resource Languages through LLM-Based Selective Translation: A Systematic Study

TL;DR

This paper systematically studies LLM-based selective translation to improve low-resource language alignment, demonstrating its effectiveness over standard translation methods and highlighting the importance of filtering noisy outputs.

Contribution

It introduces and evaluates LLM-based selective translation for low-resource language alignment, showing its advantages over vanilla translation techniques.

Findings

Selective translation improves alignment quality in low-resource languages.

Filtering noisy outputs enhances translation effectiveness.

Mixing translated and original data benefits model alignment.

Abstract

Multilingual large language models (LLMs) often demonstrate a performance gap between English and non-English languages, particularly in low-resource settings. Aligning these models to low-resource languages is essential yet challenging due to limited high-quality data. While English alignment datasets are readily available, curating equivalent data in other languages is expensive and time-consuming. A common workaround is to translate existing English alignment data; however, standard translation techniques often fail to preserve critical elements such as code, mathematical expressions, and structured formats like JSON. In this work, we investigate LLM-based selective translation, a technique that selectively translates only the translatable parts of a text while preserving non-translatable content and sentence structure. We conduct a systematic study to explore key questions around this approach, including its effectiveness compared to vanilla translation, the importance of filtering noisy outputs, and the benefits of mixing translated samples with original English data during alignment. Our experiments focus on the low-resource Indic language Hindi and compare translations generated by Google Cloud Translation (GCP) and Llama-3.1-405B. The results highlight the promise of selective translation as a practical and effective method for improving multilingual alignment in LLMs.