Ever wondered how global e-commerce platforms tackle the challenge of answering product questions in a multitude of languages? A new research paper, "Unlocking Markets: A Multilingual Benchmark to Cross-Market Question Answering," explores how AI can bridge language barriers and information gaps. The sheer volume of product questions posted daily on platforms like Amazon presents a significant hurdle. This research introduces a novel approach: using data from a resource-rich marketplace (like Amazon US) to answer questions in smaller, less resource-intensive marketplaces, even in different languages. Imagine a French shopper asking a question about a product. The answer might lie within reviews of a similar product on Amazon US! This cross-market approach is at the heart of the Multilingual Cross-market Product-based Question Answering (MCPQA) task. Researchers built a massive dataset, McMarket, spanning 7 million questions from 17 marketplaces in 11 languages. They even tapped into the power of large language models (LLMs) to label data for two key tasks: generating answers from reviews and ranking related questions to find the best answer. Early experiments show promising results, particularly in low-resource marketplaces, proving the value of shared knowledge across borders. While translating product information into English is a helpful step, the research intriguingly suggests that models perform even better when trained on the original language data, especially for non-Latin languages. This research opens doors to create more efficient and helpful customer experiences across the global e-commerce landscape. Imagine seamless assistance for shoppers worldwide, no matter their language. This vision might be closer than we think.