Large language models (LLMs) have achieved commendable accomplishments in
various natural language processing tasks. However, LLMs still encounter
significant challenges when dealing with complex scenarios involving multiple
entities. These challenges arise from the presence of implicit relationships
that demand multi-step reasoning. In this paper, we propose a novel approach
ERA-CoT, which aids LLMs in understanding context by capturing relationships
between entities and supports the reasoning of diverse tasks through
Chain-of-Thoughts (CoT). Experimental results show that ERA-CoT demonstrates
the superior performance of our proposed method compared to current CoT
prompting methods, achieving a significant improvement of an average of 5.1\%
on GPT3.5 compared to previous SOTA baselines. Our analysis indicates that
ERA-CoT increases the LLM's understanding of entity relationships,
significantly improves the accuracy of question answering, and enhances the
reasoning ability of LLMs.

我们提出了一种新的方法 ERA-CoT，通过捕捉实体之间的关系并通过思维链条（CoT）支持多样化任务的推理，从而帮助大型语言模型（LLMs）理解上下文，实现了在 GPT3.5 上相较于现有的 CoT 提示方法，平均提升了 5.1％的显著改进，增加了 LLMs 对实体关系的理解，显著提高了问答的准确性和推理能力。