用大模型挖掘文本中隐含的因果链，补全事件间的潜在因果路径。
Implicit Causal Graph Construction in Text via Chain Discovery
- 将因果关系建模为链式推理，通过LLM推断中间因果事件。
- 在1560对科学验证的因果对上验证，链式发现优于端到端构建。
- 多模型协同推理提升可靠性，适合无真实图的数据场景。
文本中的因果图通常依赖可观察的预定义事件。本文研究从文本中隐式构建因果图，将每个因果对视为潜在因果图的起点与终点，并利用大语言模型（LLMs）推断中间因果事件。对比了端到端图构建与因果链发现两种方法：后者通过聚合推断出的因果链或迭代扩展部分链来构建图。进一步探索了后验聚合与协作推理下的‘群体智慧’扩展方法。通过一个包含1560个科学验证因果对的手动标注数据库评估推断因果关系的有效性。该评估方法被提出为可靠、资源高效且可迁移至缺乏真实图的场景。
原文摘要 · Abstract (English)
Causal graphs in text are typically populated by observable, predefined events. In contrast, we study implicit causal graph construction from text by treating each described cause-effect pair as the begin- and endpoint of an underlying latent causal graph and using large language models (LLMs) to infer intermediate causal events. We compare end-to-end graph construction with methods that frame the task as causal chain discovery. In the latter, graphs are built either by aggregating inferred chains or by progressively expanding partial chains through an iterative search process. We further explore Wisdom of the Crowd extensions that access causal knowledge from multiple LLMs in post-hoc aggregation and collaborative inference settings. We analyze trade-offs among these approaches and evaluate the validity of inferred causal relations using a manually curated database of 1,560 scientifically validated causal pairs. This database-based evaluation is proposed as reliable, resource-efficient, and transferable to settings where ground-truth graphs are unavailable.
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