Blessing of Multilinguality: A Systematic Analysis of Multilingual In-Context Learning
Authors: Yilei Tu, Andrew Xue, Freda Shi
Accepted at: acl25


Reviewer paper summaries: The paper investigates multilingual in-context learning for large language models, focusing on how demonstrations in high-resource languages can enhance cross-lingual transfer to low-resource languages. The authors conduct systematic experiments comparing different multilingual prompting strategies. They find that multilingual demonstrations consistently outperform English-only ones, suggesting that multilingual exposure itself benefits cross-lingual learning.
Summary of strengths: - The paper is well-written and easy to understand.

- The authors conduct extensive experiments across multiple models (open-source and closed-source) and diverse datasets, ensuring broad applicability.

- The analysis of different multilingual prompting strategies (English-only vs. HRL-only vs. mixed-HRL) is valuable for guiding future multilingual model design.
Summary of weaknesses: - **Limited discussion on potential downsides**: The paper does not fully explore whether introducing multilingual demonstrations could induce language confusion in certain cases; While the authors show performance improvements, they do not analyze whether there are trade-offs (e.g., potential negative effects on English or HRL performance).

- **Scope could be extended**: The study does not examine how semantic similarity between demonstrations and test examples affects performance in different prompting modes.
Comments, suggestions and typos: - Does introducing multilingual demonstrations induce language confusion in some cases?
- Are the model answers in Figure 1(b) the result of a translation into English, or the original output of the model? For the Thai question, did the model answer in English (indicating language confusion)? The example in the figure is somewhat unclear.
- The work provides empirical findings but lacks a theoretical explanation for why multilingual exposure itself (even with irrelevant content) improves LLM performance. A discussion on potential underlying mechanisms (e.g., increased linguistic generalization, latent space alignment) would strengthen the contribution.
- Figure 1 caption: "Multilingual ICL mode (b) are more effective..." → "Multilingual ICL mode (b) is more effective..."
Overall assessment scores: 3.5