Cost-Efficient Cross-Lingual Retrieval-Augmented Generation for Low-Resource Languages: A Case Study in Bengali Agricultural Advisory

2026-01-05 · Md. Asif Hossain, Nabil Subhan, Mantasha Rahman Mahi, Jannatul Ferdous Nabila arxiv

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

📄 PDF Abstract BibTeX arXiv:2601.02065

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XProvence: Zero-Cost Multilingual Context Pruning for Retrieval-Augmented Generation

2026-01-26 · Youssef Mohamed, Mohamed Elhoseiny, Thibault Formal, Nadezhda Chirkova arxiv

This paper introduces XProvence, a multilingual zero-cost context pruning model for retrieval-augmented generation (RAG), trained on 16 languages and supporting 100+ languages through effective cross-lingual transfer. Mo…

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ORPHEAS: A Cross-Lingual Greek-English Embedding Model for Retrieval-Augmented Generation

2026-04-22 · Ioannis E. Livieris, Athanasios Koursaris, Alexandra Apostolopoulou, Konstantinos Kanaris Dimitris Tsakalidis 외 arxiv

Effective retrieval-augmented generation across bilingual Greek--English applications requires embedding models capable of capturing both domain-specific semantic relationships and cross-lingual semantic alignment. Exist…

XRAG: Cross-lingual Retrieval-Augmented Generation

2025-05-15 · Wei Liu, Sony Trenous, Leonardo F. R. Ribeiro, Bill Byrne 외

We propose XRAG, a novel benchmark designed to evaluate the generation abilities of LLMs in cross-lingual Retrieval-Augmented Generation (RAG) settings where the user language does not match the retrieval results. XRAG i…

Articles RAG Retrieval Retrieval-augmented Generation

From Classification to Generation: Insights into Crosslingual Retrieval Augmented ICL

2023-11-11 · Xiaoqian Li, Ercong Nie, Sheng Liang

The remarkable ability of Large Language Models (LLMs) to understand and follow instructions has sometimes been limited by their in-context learning (ICL) performance in low-resource languages. To address this, we introd…

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CONCAP: Seeing Beyond English with Concepts Retrieval-Augmented Captioning

2025-07-27 · George Ibrahim, Rita Ramos, Yova Kementchedjhieva arxiv

Multilingual vision-language models have made significant strides in image captioning, yet they still lag behind their English counterparts due to limited multilingual training data and costly large-scale model parameter…

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