LEGO : A Multi-agent Collaborative Framework with Role-playing and Iterative Feedback for Causality Explanation Generation (2023)

Abstract: Causality Explanation Generation refers to generate an explanation in natural language given an initial cause-effect pair. It demands rigorous explicit rationales to demonstrate the acquisition of implicit commonsense knowledge, which is unlikely to be easily memorized, making it challenging for large language models since they are often suffering from spurious causal associations when they encounter the content that does not exist in their memory. In this work, we introduce LEGO, a Multi-agent Collaborative Framework with Role-playing and Iterative Feedback for causality explanation generation. Specifically, we treat LLM as character malleable LEGO block and utilize role-playing to assign specific roles to five LLMs. We firstly devise a Fine-grained World Knowledge Integration Module to augment information about tasks for alleviating the phenomenon of spurious causal associations. Then, we leverage an Iterative Feedback and Refinement Module to improve the generated explanation by multi-aspect feedback. Extensive experiments on widely used WIKIWHY and e-CARE datasets show the superiority of our multi-agent framework in terms of reasoning about the causality among cause and effect.

## introduction causality explanation generation is a generative task that aims to explain why the given cause-effect pair is true using natural language. for example, given the cause clause c and effect clause e in figure 1 , a corresponding explanation x needs to be generated is "a monsoon was striking ceylon or southern india at the time and the fleet of the seventh voyage of the ming treasure voyage did not want to be caught in the storm". causality explanation generation can facilitate various applications, including explainable question answering @xcite , complex reasoning @xcite and future event prediction @xcite . cause 𝑪: the fleet of the seventh voyage of the ming treasure voyages was sailing under favorable conditions and running before the southwest monsoon on their homeward voyage. effect 𝑬: the fleet of the seventh voyage of the ming treasure voyages did not stop at ceylon or southern india. ## methodology in this section, we introduce our multi-agent collaborative framework lego. as shown in figure 2 , our framework consists of two major components involving five llms: (1) fine-grained world knowledge integration module, which augments information about tasks through the interaction of three agents; (2) iterative feedback and refinement module, which utilizes one llm serve as explainer to generate an initial output, and iteratively receives observation and commonsense feedback from critic llm to refine its explanation. ## conclusion we introduce lego, a multi-agent collaborative framework with role-playing and iterative feedback for causality explanation generation. specifically, we treat llm as character malleable lego block and utilize role-playing to assign specific roles to five llms, i.e. cause analyst, effect analyst, knowledge master, critic and explainer. we devise a fine-grained world knowledge integration module to augment information about tasks for alleviating the phenomenon of spurious causal associations. then, we leverage an iterative feedback and refinement module to improve the generated explanation by multi-aspect feedback. extensive experiments on wikiwhy and e-care show the superiority of our multi-agent framework in terms of causality explanation generation.