System Report for CCL24-Eval Task 8: Exploring Faithful and Informative Commonsense Reasoning and Moral Understanding in Children’s Stories

Commonsense reasoning and moral understanding are crucial tasks in artificial intelligence (AI) and natural language processing (NLP). However, existing research often falls short in terms of faithfulness and informativeness during the reasoning process. We propose a novel framework for performing commonsense reasoning and moral understanding using large language models (LLMs), involving constructing guided prompts by incorporating relevant knowledge for commonsense reasoning and extracting facts from stories for moral understanding. We conduct extensive experiments on the Commonsense Reasoning and Moral Understanding in Children’s Stories (CRMUS) dataset with widely recognised LLMs under both zero-shot and fine-tuning settings, demonstrating the effectiveness of our proposed method. Furthermore, we analyse the adaptability of different LLMs in extracting facts for moral understanding performance.