Where Does Retrieval Fail? Evaluating RAG Architectures for Agricultural Advisory
Retrieval quality in RAG systems is commonly reported as a single aggregate score, which can hide large differences across query types and language conditions. We study this problem in Bengali agricultural advisory, where farmer queries are often colloquial while official advisory documents use formal scientific terminology. We construct a test collection of 1,000 queries and 2,882 knowledge nodes extracted from 284 official Bangladeshi agricultural publications, and use it to evaluate five retrieval architectures and six embedding models under three controlled language conditions. The results show that no single retrieval method is consistently best. For native Bengali queries, BM25 is t...
| Authors | Khan Raiyan Ibne Reza, Sanjana Aktar Maria, Sumaiya Tabassum Nimi |
|---|---|
| Primary category | cs.CL |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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