低成本的跨语言检索增强生成用于低资源语言：孟加拉语农业咨询案例研究
Cost-Efficient Cross-Lingual Retrieval-Augmented Generation for Low-Resource Languages: A Case Study in Bengali Agricultural Advisory
AI总结：
本文提出了一种低成本的跨语言检索增强生成框架，用于孟加拉语农业咨询，通过翻译和领域关键词注入提升生成质量，实现低资源语言下的高效农业知识访问。
摘要：
在许多发展地区，获取可靠的农业咨询仍然受到持续的语言障碍限制：权威的农业手册主要用英语编写，而农民主要用低资源的本地语言如孟加拉语交流。尽管最近在大语言模型（LLMs）上的进展使自然语言交互成为可能，但直接在低资源语言中生成往往表现出不流畅和事实不一致的问题，而基于云的解决方案仍然成本过高。本文提出了一种低成本的跨语言检索增强生成（RAG）框架，用于孟加拉语农业咨询，强调事实基础和实际部署性。所提出的系统采用以翻译为中心的架构，在将孟加拉语用户查询翻译成英语后，通过领域特定关键词注入进行丰富，以将通俗的农民术语与科学术语对齐，并通过在精心编纂的英语农业手册（FAO、IRRI）上的密集向量检索进行回答。生成的英文回应随后被翻译回孟加拉语以确保可访问性。该系统完全使用开源模型实现，并在消费级硬件上运行，不依赖付费API。实验评估显示，系统能够提供可靠的来源基础回应，对超出领域的问题具有良好的拒绝能力，并且端到端延迟平均低于20秒。结果表明，结合跨语言检索与受控翻译提供了一种在低资源语言环境中获取农业知识的实用且可扩展的解决方案。
英文摘要：
Access to reliable agricultural advisory remains limited in many developing regions due to a persistent language barrier: authoritative agricultural manuals are predominantly written in English, while farmers primarily communicate in low-resource local languages such as Bengali. Although recent advances in Large Language Models (LLMs) enable natural language interaction, direct generation in low-resource languages often exhibits poor fluency and factual inconsistency, while cloud-based solutions remain cost-prohibitive. This paper presents a cost-efficient, cross-lingual Retrieval-Augmented Generation (RAG) framework for Bengali agricultural advisory that emphasizes factual grounding and practical deployability. The proposed system adopts a translation-centric architecture in which Bengali user queries are translated into English, enriched through domain-specific keyword injection to align colloquial farmer terminology with scientific nomenclature, and answered via dense vector retrieval over a curated corpus of English agricultural manuals (FAO, IRRI). The generated English response is subsequently translated back into Bengali to ensure accessibility. The system is implemented entirely using open-source models and operates on consumer-grade hardware without reliance on paid APIs. Experimental evaluation demonstrates reliable source-grounded responses, robust rejection of out-of-domain queries, and an average end-to-end latency below 20 seconds. The results indicate that cross-lingual retrieval combined with controlled translation offers a practical and scalable solution for agricultural knowledge access in low-resource language settings
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
