In recent years, large language models (LLMs) have demonstrated exponential improvements that promise transformative opportunities across various industries. Their ability to generate human-like text and ensure continuous availability facilitates the creation of interactive service chatbots aimed at enhancing customer experience and streamlining enterprise operations. Despite their potential, LLMs face critical challenges, such as a susceptibility to hallucinations and difficulties handling complex linguistic scenarios, notably code switching and dialectal variations. To address these challenges, this paper describes the design of a multilingual chatbot for Bengali-English customer service interactions utilizing retrieval-augmented generation (RAG) and targeted prompt engineering. This research provides valuable insights for the human-computer interaction (HCI) community, emphasizing the importance of designing systems that accommodate linguistic diversity to benefit both customers and businesses. By addressing the intersection of generative AI and cultural heterogeneity, this late-breaking work inspires future innovations in multilingual and multicultural HCI.

翻译：近年来，大型语言模型（LLMs）展现出指数级的性能提升，为各行业带来了变革性机遇。其生成类人文本的能力与持续可用性，促进了交互式服务聊天机器人的开发，旨在提升客户体验并优化企业运营。尽管潜力巨大，LLMs仍面临关键挑战，例如易产生幻觉以及难以处理复杂的语言场景，尤其是语码转换和方言变体。为应对这些挑战，本文描述了一种利用检索增强生成（RAG）与针对性提示工程、面向孟加拉语-英语客户服务交互的多语言聊天机器人设计方案。本研究为人机交互（HCI）领域提供了有价值的见解，强调了设计能够适应语言多样性的系统对客户与企业的双重重要性。通过探讨生成式人工智能与文化异质性的交叉点，这项最新研究为未来多语言与跨文化人机交互的创新提供了启示。