用小模型+新闻数据，让极端天气分析更精准。
ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics
- 用大模型生成推理路径，指导小模型学习极端天气逻辑。
- 在三个任务上超越专用模型，提升真实场景适用性。
- 适合气候研究、灾害预警和政策制定者使用。
准确评估极端天气事件对科研与政策至关重要，但全球多地仍缺乏本地化、细粒度的数据。这一数据缺口限制了我们对极端天气后果的分析能力，影响决策效率。大型语言模型（LLMs）可处理海量非结构化文本，提取关键信息并生成综合评估，且能将通用语言理解迁移到小型模型（SLMs），使其在特定任务中保留核心知识。本文提出极端天气推理感知对齐方法（EWRA），通过引入由大模型生成的结构化推理路径，增强小模型在极端天气分析中的表现；同时构建了大规模新闻数据集ExtremeWeatherNews。EWRA与ExtremeWeatherNews共同构成框架ClimaEmpact，聚焦三类关键任务：具体脆弱性/影响分类、主题标签和情绪分析。通过在ExtremeWeatherNews及其衍生数据集ExtremeAlign上对齐小模型，该方法显著提升其生成领域一致、有依据响应的能力。实验表明，该方法使小模型输出更具领域针对性，性能优于专用模型，在实际应用中具有更强可行性。
原文摘要 · Abstract (English)
Accurate assessments of extreme weather events are vital for research and policy, yet localized and granular data remain scarce in many parts of the world. This data gap limits our ability to analyze potential outcomes and implications of extreme weather events, hindering effective decision-making. Large Language Models (LLMs) can process vast amounts of unstructured text data, extract meaningful insights, and generate detailed assessments by synthesizing information from multiple sources. Furthermore, LLMs can seamlessly transfer their general language understanding to smaller models, enabling these models to retain key knowledge while being fine-tuned for specific tasks. In this paper, we propose Extreme Weather Reasoning-Aware Alignment (EWRA), a method that enhances small language models (SLMs) by incorporating structured reasoning paths derived from LLMs, and ExtremeWeatherNews, a large dataset of extreme weather event-related news articles. EWRA and ExtremeWeatherNews together form the overall framework, ClimaEmpact, that focuses on addressing three critical extreme-weather tasks: categorization of tangible vulnerabilities/impacts, topic labeling, and emotion analysis. By aligning SLMs with advanced reasoning strategies on ExtremeWeatherNews (and its derived dataset ExtremeAlign used specifically for SLM alignment), EWRA improves the SLMs' ability to generate well-grounded and domain-specific responses for extreme weather analytics. Our results show that the approach proposed guides SLMs to output domain-aligned responses, surpassing the performance of task-specific models and offering enhanced real-world applicability for extreme weather analytics.
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