KrishokChat: A Citation-Grounded Dataset and Benchmark for Bengali Agricultural Advisory
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Computer Science > Machine Learning
Title:KrishokChat: A Citation-Grounded Dataset and Benchmark for Bengali Agricultural Advisory
Abstract:We present KrishokChat, the first citation-grounded Bengali agricultural instruction-tuning dataset for crop advisory in low-resource settings. We establish a foundation of 290 hierarchical Knowledge Nodes, extracting disease symptoms, management practices, chemical dosages, and verbatim citations from 129 domain-filtered agricultural manuals. Every training instance inherits a verified citation header, guaranteeing 100% citation provenance. Using a Partitioned Seed Generation Matrix, these nodes are expanded into 139,200 supervised fine-tuning pairs, and augmented with 5,300 chemical safety and 1,000 adversarial safety instances, yielding 145,500 QA pairs across 18 crop categories. To evaluate real-world performance, we introduce the Farmer Benchmark, comprising 1,001 authentic farmer queries curated from field surveys and digital portals. Empirical evaluation on Gemma-4-E2B reveals that while fine-tuning on KrishokChat vastly improves structured formatting, standalone models still struggle with exact chemical dosage generalization. This highlights the dataset's true value as a verified knowledge base for retrieval-augmented generation (RAG) rather than mere parametric memorization. All data, code, and benchmarks are released under CC-BY-4.0.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.29243 [cs.LG] |
| (or arXiv:2606.29243v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.29243
arXiv-issued DOI via DataCite (pending registration)
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