面向多轮农业问答的意图感知上下文检索
Intent Aware Context Retrieval for Multi-Turn Agricultural Question Answering
AI总结：
本文提出AI农业聊天机器人Krishi Sathi，结合意图驱动的多轮对话、指令调优模型和检索增强生成，为印度农民提供个性化农业建议，实现97.53%的查询准确率和91.35%的上下文相关性。
摘要：
印度农民往往缺乏及时、可获取且语言友好的农业建议，尤其是在识字率较低的农村地区。为了解决这一可及性差距，本文提出了一种新颖的AI驱动的农业聊天机器人Krishi Sathi，旨在通过文本和语音两种方式为印度农民提供个性化、易于理解的答案。该系统的智能源于一个IFT模型，随后通过在三个精选数据集上对印度农业知识进行微调而得到优化。与回应一次性问题的传统聊天机器人不同，Krishi Sathi遵循结构化的多轮对话流程，逐步收集农民的必要细节，确保在生成回复之前完全理解查询。一旦提取了意图和上下文，系统执行检索增强生成（RAG），首先从精选的农业数据库中获取信息，然后使用IFT模型生成量身定制的回复。该聊天机器人支持英语和印地语，并具备语音输入和输出功能（通过ASR和TTS），以便于识字率低或数字技能有限的用户使用。这项工作展示了如何将意图驱动的对话流程、指令调优模型和基于检索的生成相结合，以提高印度数字农业支持的质量和可及性。该方法取得了强劲的结果，系统实现了97.53%的查询响应准确率、91.35%的上下文相关性和个性化程度，以及97.53%的查询完成率。平均响应时间保持在6秒以内，确保在英语和印地语交互中为用户提供及时支持。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
