###### Abstract
These models demonstrated impressive results across various multilingual NLP tasks, with their performance on cross-lingual understanding and generation tasks suggesting significant potential for enhancing XLS capabilities, especially for context length and languages with limited resources [15; 16]. This potential has led to increased interest in retrieval-based approaches that can better leverage the capabilities of LLMs. Few-shot in-context learning has emerged as a powerful paradigm for leveraging LLMs in various NLP tasks without task-specific fine-tuning [17]. This approach involves providing the model with a few examples of the task before asking it to perform the same task on a new input. The effectiveness of few-shot learning heavily depends on the quality and relevance of the provided examples, especially in cross-lingual contexts where linguistic nuances vary significantly. Recognizing this dependency on example quality, retrieval-augmented in-context learning methods have gained prominence [18], as these methods dynamically select relevant examples from a larger pool to construct the prompt, often yielding substantial performance improvements over using fixed or randomly selected examples [19]. Building upon this concept, recent research has explored sophisticated retrieval techniques specifically designed to identify the most informative demonstrations [20]. The selection of appropriate examples can dramatically impact performance, particularly within the cross-lingual domain, where cross-lingual retrieval approaches have employed dual retrieval strategies to source effective demonstrations from high-resource languages to bolster performance on low-resource language tasks [21]. Such cross-lingual transfer is fun damental to improving XLS, aligning closely with the objectives of leveraging knowledge from well-resourced languages. Despite the promise of these techniques, their specific application and optimization for the nuances of XLS remain underexplored, as many existing retrieval approaches prioritize fitting examples within limited prompt lengths, sometimes resorting to shorter or randomly sampled demonstrations rather than optimizing for semantic relevance crucial for cross-lingual transfer. This gap in the literature highlights a critical opportunity for improvement in cross-lingual summarization systems. As the field moves toward larger and more capable language models, the potential impact of intelligently selected examples becomes increasingly important to explore. Current explorations of LLMs for XLS have primarily focused on model selection and demonstration quantity rather than quality, leaving the critical question of demonstration selection--specifically, how to identify the most informative examples for a given test article--largely unexplored. To address these limitations, we propose MuRXLS (Multilingual Retrieval-based Cross-lingual Summarization), an enhanced framework for XLS that leverages multilingual retrieval techniques to dynamically select the most relevant examples for each query article. While existing few-shot learning approaches for XLS have been constrained by context length limitations, often selecting examples based primarily on random sampling or shortest token counts rather than relevance, our approach employs multilingual embedding models to compute article embeddings and identify the closest matching examples based on semantic similarity. This retrieval-based method ensures that each article is paired with contextually appropriate demonstrations that can better guide the LLM in generating accurate cross-lingual summaries. Rather than selecting examples based merely on length constraints or random sampling, we propose a semantic similarity framework that identifies truly analogous examples across linguistic boundaries, substantially enhancing the effectiveness of in-context learning for this complex task. Our comprehensive evaluation demonstrates the effectiveness of this approach across multiple dimensions.