为气候决策设计可信赖的文档问答系统，防止幻觉并提升准确性
Responsible Retrieval Augmented Generation for Climate Decision Making from Documents
- 针对气候文件设计专用评估框架，检测生成内容可靠性
- 在法律政策文档上验证，显著降低信息错误率
- 开源数据集与工具，适合政策研究与可信AI开发者
气候决策受限于长篇、技术性强且多语言文档中的信息复杂性与难获取性。生成式AI虽能提升信息可及性，但存在幻觉、输出难以控制及特定领域性能下降等问题。为此，本文提出面向气候文档的专用评估框架，用于评测检索增强生成（RAG）方法的检索与生成质量，并在原型工具中验证其对气候法律与政策文档问答的有效性。同时发布人工标注数据集与可扩展自动化评估工具，以推动该类系统在气候领域的广泛应用与可靠评估。研究揭示了负责任部署RAG的关键要素，并提供高风险领域中建立用户信任的用户体验设计启示。
原文摘要 · Abstract (English)
Climate decision making is constrained by the complexity and inaccessibility of key information within lengthy, technical, and multi-lingual documents. Generative AI technologies offer a promising route for improving the accessibility of information contained within these documents, but suffer from limitations. These include (1) a tendency to hallucinate or mis-represent information, (2) difficulty in steering or guaranteeing properties of generated output, and (3) reduced performance in specific technical domains. To address these challenges, we introduce a novel evaluation framework with domain-specific dimensions tailored for climate-related documents. We then apply this framework to evaluate Retrieval-Augmented Generation (RAG) approaches and assess retrieval- and generation-quality within a prototype tool that answers questions about individual climate law and policy documents. In addition, we publish a human-annotated dataset and scalable automated evaluation tools, with the aim of facilitating broader adoption and robust assessment of these systems in the climate domain. Our findings highlight the key components of responsible deployment of RAG to enhance decision-making, while also providing insights into user experience (UX) considerations for safely deploying such systems to build trust with users in high-risk domains.
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