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Large language models (LLMs) have gained enormous attention from bothacademia and industry, due to their exceptional ability in language generationand extremely powerful generalization. However, current LLMs still outputunreliable content in practical reasoning tasks due to their inherent issues(e.g., hallucination). To better disentangle this problem, in this paper, weconduct an in-depth investigation to systematically explore the capability ofLLMs in logical reasoning. More in detail, we first investigate the deficiencyof LLMs in logical reasoning on different tasks, including event relationextraction and deductive reasoning. Our study demonstrates that LLMs are notgood reasoners in solving tasks with rigorous reasoning and will producecounterfactual answers, which require us to iteratively refine. Therefore, wecomprehensively explore different strategies to endow LLMs with logicalreasoning ability, and thus enable them to generate more logically consistentanswers across different scenarios. Based on our approach, we also contribute asynthesized dataset (LLM-LR) involving multi-hop reasoning for evaluation andpre-training. Extensive quantitative and qualitative analyses on differenttasks also validate the effectiveness and necessity of teaching LLMs with logicand provide insights for solving practical tasks with LLMs in future work.

Learning To Teach Large Language Models Logical Reasoning