**ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis** **Authors:** Yanming Liu, Xinyue Peng, Tianyu Du, Jianwei Yin, Weihao Liu, Xuhong Zhang **Institution:** Zhejiang University and Southeast University **Abstract:** Large language models (LLMs) have achieved remarkable success in various natural language processing tasks, but they still face significant challenges when dealing with complex scenarios involving multiple entities. These challenges arise from implicit relationships that require multi-step reasoning. This paper introduces ERA-CoT, a novel approach that enhances LLMs' understanding of context by capturing relationships between entities and supports diverse tasks through Chain-of-Thought (CoT). Experimental results show that ERA-CoT outperforms current CoT prompting methods, achieving a significant improvement of 5.1% on GPT3.5 compared to previous state-of-the-art baselines. The analysis indicates that ERA-CoT increases the LLM's understanding of entity relationships, significantly improves question answering accuracy, and enhances reasoning ability. **Introduction:** LLMs have shown remarkable in-context learning capabilities in various NLP tasks. However, they still struggle with complex scenarios involving multiple entities and implicit relationships. Named Entity Recognition (NER) and Relation Extraction methods have been used to address these challenges, but they have limitations. ERA-CoT addresses this by extracting entities, inferring explicit and implicit relationships, and scoring and filtering these relationships to enhance reasoning and comprehension. **Methodology:** ERA-CoT consists of five stages: entity extraction, explicit relationship extraction, implicit relationship inference, relationship discrimination, and question answering. Each stage enhances the model's understanding of entity relations, allowing it to make more accurate predictions. **Experiments:** The method was evaluated on six datasets, including commonsense reasoning, logical reasoning, and mathematical reasoning. ERA-CoT outperformed all baselines on nearly all benchmarks, achieving an average improvement of 5.1%. Ablation studies and error analysis further validate the effectiveness of each component of ERA-CoT. **Conclusion:** ERA-CoT significantly improves LLMs' performance in open-domain question answering and knowledge reasoning tasks by leveraging entity relationship analysis. The method's versatility and effectiveness in various domains make it a valuable contribution to the field of NLP.**ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis** **Authors:** Yanming Liu, Xinyue Peng, Tianyu Du, Jianwei Yin, Weihao Liu, Xuhong Zhang **Institution:** Zhejiang University and Southeast University **Abstract:** Large language models (LLMs) have achieved remarkable success in various natural language processing tasks, but they still face significant challenges when dealing with complex scenarios involving multiple entities. These challenges arise from implicit relationships that require multi-step reasoning. This paper introduces ERA-CoT, a novel approach that enhances LLMs' understanding of context by capturing relationships between entities and supports diverse tasks through Chain-of-Thought (CoT). Experimental results show that ERA-CoT outperforms current CoT prompting methods, achieving a significant improvement of 5.1% on GPT3.5 compared to previous state-of-the-art baselines. The analysis indicates that ERA-CoT increases the LLM's understanding of entity relationships, significantly improves question answering accuracy, and enhances reasoning ability. **Introduction:** LLMs have shown remarkable in-context learning capabilities in various NLP tasks. However, they still struggle with complex scenarios involving multiple entities and implicit relationships. Named Entity Recognition (NER) and Relation Extraction methods have been used to address these challenges, but they have limitations. ERA-CoT addresses this by extracting entities, inferring explicit and implicit relationships, and scoring and filtering these relationships to enhance reasoning and comprehension. **Methodology:** ERA-CoT consists of five stages: entity extraction, explicit relationship extraction, implicit relationship inference, relationship discrimination, and question answering. Each stage enhances the model's understanding of entity relations, allowing it to make more accurate predictions. **Experiments:** The method was evaluated on six datasets, including commonsense reasoning, logical reasoning, and mathematical reasoning. ERA-CoT outperformed all baselines on nearly all benchmarks, achieving an average improvement of 5.1%. Ablation studies and error analysis further validate the effectiveness of each component of ERA-CoT. **Conclusion:** ERA-CoT significantly improves LLMs' performance in open-domain question answering and knowledge reasoning tasks by leveraging entity relationship analysis. The method's versatility and effectiveness in various domains make it a valuable contribution to the field of NLP.