用多源证据模糊聚合，让大模型零样本识别事件因果关系更准
Zero-Shot Event Causality Identification via Multi-source Evidence Fuzzy Aggregation with Large Language Models
- 拆解因果推理为时序、必要性、充分性三任务+辅助任务
- 在三个数据集上F1提升6.2%，精度提高9.3%，误判率显著降低
- 适合需要零样本因果分析的研究者，尤其关注可解释性与可靠性
事件因果识别（ECI）旨在文本中检测事件间的因果关系。现有模型多依赖标注数据，而大语言模型虽支持零样本推理，却易产生因果幻觉，错误建立虚假因果链。为此，本文提出基于多源证据模糊聚合的零样本框架MEFA。首先，将因果推理分解为时序判断、必要性分析和充分性验证三主任务，并辅以三项辅助任务；其次，通过精心设计提示词，引导大模型输出不确定性响应与确定性结论；最后，量化子任务结果，采用模糊聚合方法整合证据，完成因果评分与判定。在三个基准数据集上的实验表明，MEFA相比最优无监督基线，F1得分提升6.2%，精度提升9.3%，同时显著减少由幻觉引发的错误。深入分析验证了任务分解的有效性及模糊聚合的优越性。
原文摘要 · Abstract (English)
Event Causality Identification (ECI) aims to detect causal relationships between events in textual contexts. Existing ECI models predominantly rely on supervised methodologies, suffering from dependence on large-scale annotated data. Although Large Language Models (LLMs) enable zero-shot ECI, they are prone to causal hallucination-erroneously establishing spurious causal links. To address these challenges, we propose MEFA, a novel zero-shot framework based on Multi-source Evidence Fuzzy Aggregation. First, we decompose causality reasoning into three main tasks (temporality determination, necessity analysis, and sufficiency verification) complemented by three auxiliary tasks. Second, leveraging meticulously designed prompts, we guide LLMs to generate uncertain responses and deterministic outputs. Finally, we quantify LLM's responses of sub-tasks and employ fuzzy aggregation to integrate these evidence for causality scoring and causality determination. Extensive experiments on three benchmarks demonstrate that MEFA outperforms second-best unsupervised baselines by 6.2% in F1-score and 9.3% in precision, while significantly reducing hallucination-induced errors. In-depth analysis verify the effectiveness of task decomposition and the superiority of fuzzy aggregation.
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