FEAMR: Enhancing Reasoning Ability of LLM Through Factual Evidence and Abstract Meaning Representation

Combining logical reasoning with large language models enhances their capacity to solve complex problems reliably and robustly. Nevertheless, previous studies for logical reasoning mainly focus on capturing entity-aware knowledge while needing help to capture logical relationships among facts. Besides, the limited labeled data further hinders the ability of data-hungry neural models. Therefore, we propose a novel symbolic-neural reasoning method based on factual evidence and Abstract Meaning Representation (AMR) to enhance the Reasoning ability of Large Language Models. The former enriches the semantics of the context to obtain implicit logical expressions according to factual evidence and logical rules. The latter exploit AMR to augment literally similar but logically different training instances for contrastive learning. Extensive experiments on benchmark datasets demonstrate that the proposed method significantly outperforms solid baselines and achieves new state-of-the-art performance.