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。

ERA-CoT: 通过实体关系分析改进思维链

ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis

To improve the ability of the large language model (LLMs) to handle complex
reasoning problems, chain-of-thoughts (CoT) methods were proposed to guide LLMs
to reason step-by-step, facilitating problem solving from simple to complex
tasks. State-of-the-art approaches for generating such a chain involve
interactive collaboration, where the learner generates candidate intermediate
thoughts, evaluated by the LLM, guiding the generation of subsequent thoughts.
However, a widespread yet understudied problem is that the evaluation from the
LLM is typically noisy and unreliable, potentially misleading the generation
process in selecting promising intermediate thoughts. In this paper, motivated
by Vapnik's principle, we propose a novel comparison-based CoT generation
algorithm that directly identifies the most promising thoughts with the noisy
feedback from the LLM. In each round, we randomly pair intermediate thoughts
and directly prompt the LLM to select the more promising one from each pair,
allowing us to identify the most promising thoughts through an iterative
process. To further model the noise in the comparison, we resort to the
techniques of ensemble and dueling bandits and propose two variants of the
proposed algorithm. Experiments on three real-world mathematical and reasoning
tasks demonstrate the effectiveness of our proposed algorithm and verify the
rationale of the direct pairwise comparison.

提出了一种基于比较的链式思考生成算法，通过直接利用语言模型的嘈杂反馈，识别最有前景的思考，从而提高大型语言模型在处理复杂推理问题时的能力。