Blessing of Multilinguality: A Systematic Analysis of Multilingual In-Context Learning

ACL ARR 2025 February Submission2690 Authors

Abstract:

While multilingual large language models generally perform adequately, and sometimes even rival English performance on high-resource languages (HRLs), they often significantly underperform on low-resource languages (LRLs). Among several prompting strategies aiming at bridging the gap, multilingual in-context learning (ICL) has been particularly effective when demonstration in target languages is unavailable. However, there lacks a systematic understanding when and why it works well.

In this work, we systematically analyze multilingual ICL, using demonstrations in HRLs to enhance cross-lingual transfer. We show that demonstrations in mixed HRLs consistently outperform English-only ones across the board, particularly for tasks written in LRLs. Surprisingly, our ablation study show that the presence of irrelevant non-English sentences in the prompt yields measurable gains, suggesting the effectiveness of multilingual exposure itself. Our results highlight the potential of strategically leveraging multilingual resources to bridge the performance gap for underrepresented languages.

Paper Type: Long

Research Area: Multilingualism and Cross-Lingual NLP

Research Area Keywords: Multilingualism and Cross-Lingual NLP

Contribution Types: Model analysis & interpretability

Languages Studied: Bulgarian, Bengali, Danish, German, English, Spanish, Estonian, Persian, French, Croatian, Haitian, Indonesian, Italian, Japanese, Korean, Dutch, Quechua, Russian, Swahili, Tamil, Telugu, Thai, Turkish, Vietnamese, Chinese

Submission Number: 2690