###### Abstract  
Cross-lingual summarization (XLS) involves generating a summary in one language from an article written in another language. XLS presents substantial hurdles due to the complex linguistic structures across languages and the challenges in transferring knowledge effectively between them. Although Large Language Models (LLMs) have demonstrated capabilities in cross-lingual tasks, the integration of retrieval-based in-context learning remains largely unexplored, despite its potential to overcome these linguistic barriers by providing relevant examples. In this paper, we introduce Multilingual Retrieval-based Cross-lingual Summarization (MuRXLS), a robust framework that dynamically selects the most relevant summarization examples for each article using multilingual retrieval. Our method leverages multilingual embedding models to identify contextually appropriate demonstrations for various LLMs. Experiments across twelve XLS setups (six language pairs in both directions) reveal a notable directional asymmetry: our approach significantly outperforms baselines in many-to-one (X\(\rightarrow\)English) scenarios, while showing comparable performance in one-to-many (English\(\rightarrow\)X) directions. We also observe a strong correlation between article-example semantic similarity and summarization quality, demonstrating that intelligently selecting contextually relevant examples substantially improves XLS performance by providing LLMs with more informative demonstrations. c 20  
rization and translation objectives [5]. These constraints prompted researchers to explore integrated solutions that could simultaneously navigate both tasks. The paradigm shift toward end-to-end architectures gained momentum with advances in multilingual representation learning [6]. Neural approaches began to directly generate cross-lingual summaries without intermediate steps, demonstrating that unified architectures could outperform pipeline approaches while requiring fewer computational resources. Building on this foundation, researchers introduced dual attention mechanisms specifically designed to align cross-lingual content during the summarization process [7]. Despite these advances, significant challenges remain for low-resource language pairs where parallel summarization data is scarce. More recently, the emergence of Large Language Models (LLMs) has opened new possibilities for addressing these challenges, as they have demonstrated promising capabilities for cross-lingual tasks [8; 9]. These studies leverage multilingual knowledge to perform zero-shot and few-shot learning across various NLP tasks, demonstrating that few-shot in-context learning can significantly enhance performance without task-specific fine-tuning [10; 11; 12]. Building on this potential, research has increasingly focused on applying LLMs to XLS tasks. Initial evaluations of GPT-3.5's zero-shot capabilities across several language pairs found promising results that approached supervised baselines for some high-resource languages. However, these approaches have primarily focused on basic example selection rather than optimizing the quality and relevance of demonstrations for cross-lingual contexts. Previous work has shown that few-shot in-context learning can significantly improve XLS performance of LLMs, particularly in low-resource settings, yet earlier approaches to few-shot learning for XLS were constrained by context length limitations, often selecting examples based primarily on random sampling rather than relevance. Specifically, prior methods typically prioritized random examples with the shortest token counts to fit within prompt constraints, limiting the semantic appropriateness of the selected demonstrations. Despite these constraints, the emergence of powerful open-source models such as Llama-3-70B-Instruct and Mistral-7B-Instruct [13; 14] have opened new possibilities for addressing these challenges.