[2603.03294] Fine-Tuning and Evaluating Conversational AI for Agricultural Advisory

Computer Science > Computation and Language arXiv:2603.03294 (cs) [Submitted on 6 Feb 2026] Title:Fine-Tuning and Evaluating Conversational AI for Agricultural Advisory Authors:Sanyam Singh, Naga Ganesh, Vineet Singh, Lakshmi Pedapudi, Ritesh Kumar, SSP Jyothi, Archana Karanam, C. Yashoda, Mettu Vijaya Rekha Reddy, Shesha Phani Debbesa, Chandan Dash View a PDF of the paper titled Fine-Tuning and Evaluating Conversational AI for Agricultural Advisory, by Sanyam Singh and 10 other authors View PDF HTML (experimental) Abstract:Large Language Models show promise for agricultural advisory, yet vanilla models exhibit unsupported recommendations, generic advice lacking specific, actionable detail, and communication styles misaligned with smallholder farmer needs. In high stakes agricultural contexts, where recommendation accuracy has direct consequences for farmer outcomes, these limitations pose challenges for responsible deployment. We present a hybrid LLM architecture that decouples factual retrieval from conversational delivery: supervised fine-tuning with LoRA on expert-curated GOLDEN FACTS (atomic, verified units of agricultural knowledge) optimizes fact recall, while a separate stitching layer transforms retrieved facts into culturally appropriate, safety-aware responses. Our evaluation framework, DG-EVAL, performs atomic fact verification (measuring recall, precision, and contradiction detection) against expert-curated ground truth rather than Wikipedia or retrieved docum...