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

Yanming Liu, Xinyue Peng, Tianyu Du, Jianwei Yin, Weihao Liu, Xuhong Zhang

TL;DR

This work addresses the difficulty LLMs face in reasoning over complex entity terrains by introducing ERA-CoT, a framework that systematically extracts entities, derives explicit and implicit relationships, and discriminates unreliable relations to guide Chain-of-Thought reasoning. It integrates named-entity recognition and relation extraction into a zero-shot CoT setting, using a five-stage process (Entities Extraction, Explicit/Implicit Relationship Handling, Discrimination, and Question Answering) and a thresholded scoring mechanism to prune uncertain relations. Empirical results across six datasets and two model families (GPT-3.5 and Llama-2-13B) show ERA-CoT yields substantial gains (average ~5.1% on GPT-3.5), with pronounced benefits in commonsense, logical, and mathematical reasoning. The findings suggest that explicit consideration of entity relationships can significantly boost LLM reasoning accuracy and robustness, particularly in complex scenarios with multiple interacting entities.

Abstract

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.

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

TL;DR

This work addresses the difficulty LLMs face in reasoning over complex entity terrains by introducing ERA-CoT, a framework that systematically extracts entities, derives explicit and implicit relationships, and discriminates unreliable relations to guide Chain-of-Thought reasoning. It integrates named-entity recognition and relation extraction into a zero-shot CoT setting, using a five-stage process (Entities Extraction, Explicit/Implicit Relationship Handling, Discrimination, and Question Answering) and a thresholded scoring mechanism to prune uncertain relations. Empirical results across six datasets and two model families (GPT-3.5 and Llama-2-13B) show ERA-CoT yields substantial gains (average ~5.1% on GPT-3.5), with pronounced benefits in commonsense, logical, and mathematical reasoning. The findings suggest that explicit consideration of entity relationships can significantly boost LLM reasoning accuracy and robustness, particularly in complex scenarios with multiple interacting entities.

Abstract

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.
Paper Structure (31 sections, 5 equations, 3 figures, 7 tables)

This paper contains 31 sections, 5 equations, 3 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Chain of thought
  4. Named Entity Recognition
  5. Relation Extraction
  6. Methodology
  7. Experimental Setup
  8. Datasets and Models
  9. Baselines
  10. Implementation
  11. Experiments
  12. Main Results
  13. Ablation Studies
  14. Analysis on Implicit Relationship
  15. Low relations density sentences analysis
  16. ...and 16 more sections

Figures (3)

  • Figure 1: The top of the figure represents the standard prediction process. The bottom of the figure shows the five-step inference process of ERA-CoT, which relies on the extraction of entities and the inference and analysis of relationships between entities to obtain the results.
  • Figure 2: Comparison on the use of SC for the first two steps. Evaluation on the final performance.
  • Figure 3: Comparison of performance between Single-Step and Multi-Step Relation Extraction. The score is evaluated on accuracy.