Kisan-BiQAS: A Bilingual Retrieval-Augmented Framework for Agricultural Question Answering System
Abstract
Abstract Agricultural advisory systems require high factual accuracy, multilingual accessibility, and robustness to noisy real-world data. This paper presents Kisan-BiQAS, a bilingual (Hindi–English) question answering framework designed for farmercentric advisory support using Kisan Call Centre (KCC) data and curated agricultural knowledge sources. The proposed system integrates retrieval-based and retrieval-augmented generation (RAG) paradigms within a unified architecture, enabling a systematic comparison between extractive and generative approaches. The framework employs dual retrieval mechanisms for English and multilingual queries, semantic embedding-based indexing, and parallel large language model (LLM) inference using LLaMA and Qwen, followed by an attention-based fusion strategy. A comprehensive evaluation is conducted using Exact Match, F1-score, BLEU, ROUGE, and additional similarity metrics. Experimental results reveal that retrieval-only configurations achieve nearperfect performance (Accuracy and F1 ≈ 1.0) with zero hallucination in a closed-domain setting, establishing an upper-bound benchmark for agricultural QA. In contrast, generative models exhibit performance degradation due to paraphrasing and hallucination effects, with LLaMA achieving moderate performance (F1 ≈ 0.45) and Qwen showing recall-heavy but low-precision behavior (F1 ≈ 0.15, hallucination ≈ 58%). Further analysis demonstrates that errors primarily originate from the generation stage rather…
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