用大模型让普通人也能轻松读懂空气质量数据。
VayuBuddy: an LLM-Powered Chatbot to Democratize Air Quality Insights
- 通过自然语言提问，自动分析政府传感器数据并生成代码。
- 在45个问题上测试7个大模型，实现问答与可视化生成。
- 适合公众、环保组织及政策制定者快速获取空气污染信息。
每年约有670万人死于空气污染。尽管政策制定者在推进缓解措施，但公众意识提升也能有效降低暴露风险。政府安装的空气质量传感器数据虽公开，但原始格式使各类利益相关方难以提取有效信息。本文提出VayuBuddy——一个基于大语言模型（LLM）的聊天机器人系统，旨在降低公众与空气质量数据之间的信息壁垒。用户以自然语言提问，系统利用大模型生成Python代码分析结构化传感器数据，并返回自然语言答案。我们使用印度政府的空气质量传感器数据，对7个大模型在45个多样化问答对上的表现进行了基准测试。此外，VayuBuddy还能自动生成折线图、地图、柱状图等多种可视化图表，展示数据趋势与空间分布。
原文摘要 · Abstract (English)
Nearly 6.7 million lives are lost due to air pollution every year. While policymakers are working on the mitigation strategies, public awareness can help reduce the exposure to air pollution. Air pollution data from government-installed sensors is often publicly available in raw format, but there is a non-trivial barrier for various stakeholders in deriving meaningful insights from that data. In this work, we present VayuBuddy, a Large Language Model (LLM)-powered chatbot system to reduce the barrier between the stakeholders and air quality sensor data. VayuBuddy receives the questions in natural language, analyses the structured sensory data with a LLM-generated Python code and provides answers in natural language. We use the data from Indian government air quality sensors. We benchmark the capabilities of 7 LLMs on 45 diverse question-answer pairs prepared by us. Additionally, VayuBuddy can also generate visual analysis such as line-plots, map plot, bar charts and many others from the sensory data as we demonstrate in this work.
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