Debate with Myself: Zero-Shot Event Causality Identification with Adversarial Evidence Integration via Large Language Models

摘要

Event Causality Identification (ECI) is a crucial task in knowledge discovery that extracts structured causal relationships between annotated event mentions from unstructured text. However, existing approaches typically rely on extensive labeled data, which is scarce for specialized domains and topics. Although Large Language Models (LLMs) show strong promise for few-shot and zero-shot information extraction, they are prone to “causal hallucination,” generating unreliable and spurious causal links. To address these limitations, we propose LLM-SD (Large Language Model Self-Debate), a novel framework that formulates ECI as a structured debate among multiple identical instances of a single LLM. Causality is determined through the integration of adversarial evidence. The framework employs LLMs in distinct roles: an affirmative team argues for the existence of causality, a negative team argues against it, and an adjudication committee evaluates the evidence for determination. An evidence strength grading rule guides the quantification and integration of adversarial evidence. The automatic and LLM-driven verification finally produce a reasoned verdict for event causality. LLM-SD reduces spurious causal links resulting from causal hallucination in LLMs and identifies more long-distance causalities by promoting a balanced evaluation of arguments. Extensive experiments on three benchmark datasets demonstrate that LLM-SD achieves state-of-the-art performance in a zero-shot setting.