为印度农民打造可语音交互的多轮农业问答机器人，提升信息获取效率。
Intent Aware Context Retrieval for Multi-Turn Agricultural Question Answering
- 通过多轮对话逐步明确农户意图与上下文，实现精准理解。
- 准确率97.53%，上下文相关性91.35%，响应时间低于6秒。
- 支持英印双语语音输入输出，适合低识字率用户使用。
印度农民在农村地区常缺乏及时、易得且语言友好的农业指导，尤其在识字率较低的地区。本文提出一个名为Krishi Sathi的AI农业聊天机器人，通过文本和语音方式为农民提供个性化、易懂的答案。系统基于IFT模型，在三个精选的印度农业知识数据集上进行微调。不同于传统单轮问答，Krishi Sathi采用结构化多轮对话流程，逐步收集必要信息以完整理解问题。在识别出用户意图与上下文后，系统通过检索增强生成（RAG）机制，从定制农业数据库中提取信息，并利用IFT模型生成定制化回答。系统支持英语和印地语，具备语音输入（ASR）与输出（TTS）功能，便于低识字或数字技能有限的用户使用。实验表明，该方法在查询应答准确率、上下文相关性与个性化方面分别达到97.53%、91.35%与97.53%，平均响应时间低于6秒，有效提升了印度数字农业支持的可及性与质量。
原文摘要 · Abstract (English)
Indian farmers often lack timely, accessible, and language-friendly agricultural advice, especially in rural areas with low literacy. To address this gap in accessibility, this paper presents a novel AI-powered agricultural chatbot, Krishi Sathi, designed to support Indian farmers by providing personalized, easy-to-understand answers to their queries through both text and speech. The system's intelligence stems from an IFT model, subsequently refined through fine-tuning on Indian agricultural knowledge across three curated datasets. Unlike traditional chatbots that respond to one-off questions, Krishi Sathi follows a structured, multi-turn conversation flow to gradually collect the necessary details from the farmer, ensuring the query is fully understood before generating a response. Once the intent and context are extracted, the system performs Retrieval-Augmented Generation (RAG) by first fetching information from a curated agricultural database and then generating a tailored response using the IFT model. The chatbot supports both English and Hindi languages, with speech input and output features (via ASR and TTS) to make it accessible for users with low literacy or limited digital skills. This work demonstrates how combining intent-driven dialogue flows, instruction-tuned models, and retrieval-based generation can improve the quality and accessibility of digital agricultural support in India. This approach yielded strong results, with the system achieving a query response accuracy of 97.53%, 91.35% contextual relevance and personalization, and a query completion rate of 97.53%. The average response time remained under 6 seconds, ensuring timely support for users across both English and Hindi interactions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。