Exploiting large language models (LLMs) to tackle deductive reasoning has
garnered growing attention. It still remains highly challenging to achieve
satisfactory results in complex deductive problems, characterized by plenty of
premises (i.e., facts or rules) entailing intricate relationships among
entities and requiring multi-hop reasoning. One intuitive solution is to
decompose the original task into smaller sub-tasks, and then chain the multiple
casual reasoning steps together in a forward (e.g., Selection-Inference) or
backward (e.g., LAMBADA) direction. However, these techniques inevitably
necessitate a large number of overall stages, leading to computationally
expensive operations and a higher possibility of making misleading steps. In
addition to stage-by-stage decomposition, we draw inspiration from another
aspect of human problem-solving. Humans tend to distill the most relevant
information and organize their thoughts systematically (e.g., creating mind
maps), which assists them in answering questions or drawing conclusions
precisely and quickly. In light of this, we propose a novel reasoning approach
named Concise and Organized Perception (COP). COP carefully analyzes the given
statements to efficiently identify the most pertinent information while
eliminating redundancy. It then prompts the LLMs in a more organized form that
adapts to the model's inference process. By perceiving concise and organized
proofs, the deductive reasoning abilities of LLMs can be better elicited, and
the risk of acquiring errors caused by excessive reasoning stages is mitigated.
Furthermore, our approach can be combined with the aforementioned ones to
further boost their performance. Extensive experimental results on three
popular deductive benchmarks (i.e., ProofWriter, PrOntoQA and PrOntoQA-OOD)
show that COP significantly outperforms previous state-of-the-art methods.

通过引入 Concise and Organized Perception (COP) 方法，可以高效地分解复杂的演绎问题并消除冗余信息，从而在与之前的方法相比表现显著优异的同时，减少错误推理的风险。