Can LLMs Identify Event Causality More Accurately through Debate? A Systematic Assessment of LLMs’ Factuality and Reasoning

Event Causality Identification (ECI) aims to determine both the existence and direction of a causal relation between two events. Given the remarkable capabilities of large language models (LLMs) in various natural language processing (NLP) tasks, several studies have explored their performance in the ECI task. However, applying LLMs to the ECI task carries risks, as their generations often contain factual and reasoning errors. Due to the powerful capabilities of debate, some studies have leveraged it to mitigate these issues. However, debate can sometimes lead LLMs to generate even more errors, suggesting that debate may not effectively enhance the quality of LLMs’ generations. Therefore, we propose a novel debate framework for LLMs in the ECI task to validate whether LLMs can identify event causality more accurately through debate. As LLMs’ generations often mix correct information with factual and reasoning errors, binary judgments on their generations may not fully reveal their potential in real-world use cases. And factual errors reflect flaws in the factuality of LLMs’ generations, while reasoning errors reveal weaknesses in their reasoning abilities. Therefore, we propose a Fine-Grained Factuality Score (FGFS) to better evaluate the factuality of LLMs’ generations and a Fine-Grained Reasoning Score (FGRS) to assess their reasoning abilities more effectively. Additionally, we redefine factuality and correct reasoning for LLMs’ generations at a fine-grained level to ensure more precise evaluation results for our metrics. Finally, considering the lack of automated evaluation for FGFS and FGRS and the time-consuming and costly nature of human evaluation, we propose automated methods for both proposed metrics and validate their superior performance by human expertise.