Structured Multi-Step Reasoning for Entity Matching Using Large Language Model

2025-11-27

AI Summary

To address the opacity and instability of large language models (LLMs) in entity matching, this paper proposes a three-stage structured multi-step reasoning framework: (1) token-level matching/non-matching identification, (2) consistency assessment of key attributes, and (3) holistic decision-making via information fusion. We further introduce a debate-style contrastive reasoning mechanism, wherein multiple complementary perspectives jointly validate intermediate conclusions to enhance robustness. This work is the first to systematically investigate *explicit* reasoning pathways for LLMs in entity matching, integrating zero-shot/few-shot prompting, structured chain-of-thought reasoning, and multi-perspective debate strategies. Evaluated on multiple real-world benchmark datasets, our method achieves significant improvements over state-of-the-art approaches—delivering both higher accuracy and enhanced interpretability. The results establish a novel paradigm for LLM-driven structured reasoning and provide empirical grounding for transparent, reliable entity matching.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Knowledge Representation and Reasoning: Argumentation

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Entity matching is a fundamental task in data cleaning and data integration. With the rapid adoption of large language models (LLMs), recent studies have explored zero-shot and few-shot prompting to improve entity matching accuracy. However, most existing approaches rely on single-step prompting and offer limited investigation into structured reasoning strategies. In this work, we investigate how to enhance LLM-based entity matching by decomposing the matching process into multiple explicit reasoning stages. We propose a three-step framework that first identifies matched and unmatched tokens between two records, then determines the attributes most influential to the matching decision, and finally predicts whether the records refer to the same real-world entity. In addition, we explore a debate-based strategy that contrasts supporting and opposing arguments to improve decision robustness. We evaluate our approaches against multiple existing baselines on several real-world entity matching benchmark datasets. Experimental results demonstrate that structured multi-step reasoning can improve matching performance in several cases, while also highlighting remaining challenges and opportunities for further refinement of reasoning-guided LLM approaches.

Problem

• Enhancing entity matching accuracy using structured multi-step reasoning with LLMs
• Decomposing matching into token identification, attribute analysis, and entity prediction
• Improving decision robustness via debate-based contrasting arguments in matching

Innovation

• Decomposes matching into explicit reasoning stages
• Uses debate strategy for robust decision-making
• Identifies influential attributes before final prediction