构建气候报告中复杂隐含因果关系的数据集，助力理解气候变化深层机制。
ClimateCause: Complex and Implicit Causal Structures in Climate Reports
- 从政策科学报告中提取高阶因果结构，标注隐含与嵌套因果关系。
- 揭示因果图语义复杂度与文本可读性相关，可量化评估信息难度。
- 发现大模型在因果链推理上仍存明显短板，是未来关键挑战。
理解气候变化需要推理复杂的因果网络，但现有因果发现数据集主要捕捉显式、直接的因果关系。我们提出ClimateCause，一个由专家人工标注的高质量数据集，源自面向政策的科学气候报告，包含隐含与嵌套因果结构。通过将因果表达标准化并拆解为独立因果关系，支持图结构构建，并对因果相关性、关系类型及时空上下文进行独特标注。我们进一步验证了ClimateCause在量化文本可读性方面的价值，基于底层因果图的语义复杂度进行评估。最后，在大语言模型上的基准测试显示，相关性推断尚可，而因果链推理仍是显著挑战。
原文摘要 · Abstract (English)
Understanding climate change requires reasoning over complex causal networks. Yet, existing causal discovery datasets predominantly capture explicit, direct causal relations. We introduce ClimateCause, a manually expert-annotated dataset of higher-order causal structures from science-for-policy climate reports, including implicit and nested causality. Cause-effect expressions are normalized and disentangled into individual causal relations to facilitate graph construction, with unique annotations for cause-effect correlation, relation type, and spatiotemporal context. We further demonstrate ClimateCause's value for quantifying readability based on the semantic complexity of causal graphs underlying a statement. Finally, large language model benchmarking on correlation inference and causal chain reasoning highlights the latter as a key challenge.
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。