Zero-Shot Event Causality Identification via Multi-source Evidence Fuzzy Aggregation with Large Language Models
Journal:
arXiv
Published Date:
Jun 6, 2025
Abstract
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.
