Large Language Models (LLMs) have demonstrated impressive capabilities across various tasks. However, LLMs often struggle with spatial reasoning which is one essential part of reasoning and inference and requires understanding complex relationships between objects in space. This paper proposes a novel neural-symbolic framework that enhances LLMs' spatial reasoning abilities. We evaluate our approach on two benchmark datasets: StepGame and SparQA, implementing three distinct strategies: (1) ASP (Answer Set Programming)-based symbolic reasoning, (2) LLM + ASP pipeline using DSPy, and (3) Fact + Logical rules. Our experiments demonstrate significant improvements over the baseline prompting methods, with accuracy increases of 40-50% on StepGame} dataset and 3-13% on the more complex SparQA dataset. The "LLM + ASP" pipeline achieves particularly strong results on the tasks of Finding Relations (FR) and Finding Block (FB) questions, though performance varies across different question types. The impressive results suggest that while neural-symbolic approaches offer promising directions for enhancing spatial reasoning in LLMs, their effectiveness depends heavily on the specific task characteristics and implementation strategies. We propose an integrated, simple yet effective set of strategies using a neural-symbolic pipeline to boost spatial reasoning abilities in LLMs. This pipeline and its strategies demonstrate strong and broader applicability to other reasoning domains in LLMs, such as temporal reasoning, deductive inference etc.

本研究针对大型语言模型在空间推理领域的不足，提出了一种新颖的神经符号框架以提升其空间推理能力。通过在StepGame和SparQA两个基准数据集上进行评估，实验结果显示，该管道在多个任务上显著提高了准确率，体现出神经符号方法在增强大型语言模型空间推理方面的潜力。

增强大型语言模型空间推理的神经符号集成管道