Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners

TL;DR

This paper investigates how instruction-tuned large language models can spontaneously improve their multilingual alignment without explicit parallel data, demonstrating their potential for better multilingual understanding and generalization.

Contribution

The study reveals that instruction-tuned LLMs can enhance multilingual alignment spontaneously, even for unseen languages, through question translation data without explicit annotation.

Findings

LLMs improve multilingual alignment without explicit parallel data.

Instruction tuning on question translation encourages cross-language alignment.

LLMs show strong potential for multilingual generalization.

Abstract

Recently, Large Language Models (LLMs) have shown impressive language capabilities. While most of the existing LLMs have very unbalanced performance across different languages, multilingual alignment based on translation parallel data is an effective method to enhance the LLMs' multilingual capabilities. In this work, we discover and comprehensively investigate the spontaneous multilingual alignment improvement of LLMs. We find that LLMs instruction-tuned on the question translation data (i.e. without annotated answers) are able to encourage the alignment between English and a wide range of languages, even including those unseen during instruction-tuning. Additionally, we utilize different settings and mechanistic interpretability methods to analyze the LLM's performance in the multilingual scenario comprehensively. Our work suggests that LLMs have enormous potential for improving multilingual alignment efficiently with great language and task generalization.