用翻译+检索让低资源语言农民也能准确获取农业知识
Cost-Efficient Cross-Lingual Retrieval-Augmented Generation for Low-Resource Languages: A Case Study in Bengali Agricultural Advisory
- 用翻译把孟加拉语问题转成英语，再用专业术语增强匹配
- 答案来自权威英文农书，生成结果事实准确，延迟低于20秒
- 全开源部署，普通电脑就能跑，适合资源有限地区使用
由于语言障碍，许多发展中国家的农民难以获取可靠农业建议：权威手册多为英文，而农民主要使用孟加拉语等低资源语言。尽管大语言模型可实现自然语言交互，但直接生成低资源语言内容常出现语义不通或事实错误，云端方案又成本高昂。本文提出一种面向孟加拉语农业咨询的低成本跨语言检索增强生成框架，强调事实准确性与实际可部署性。系统采用以翻译为核心的架构：将孟加拉语用户提问翻译为英语，通过领域关键词注入，使口语化表达与科学术语对齐；利用密集向量检索在精选英文农书（FAO、IRRI）语料库中查找答案；生成的英文回复再翻译回孟加拉语，确保可访问性。整个系统仅使用开源模型，运行于消费级硬件，无需付费API。实验表明，系统能可靠生成有来源依据的回答，对非领域问题具有强鲁棒性拒绝能力，平均端到端延迟低于20秒。结果表明，跨语言检索结合受控翻译是低资源语言农业知识获取的可行且可扩展方案。
原文摘要 · Abstract (English)
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
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