DCMKC : A Dual Consistency Matching Approach for Multi-hop Question Answering in LLM s (2025)

Abstract: Reasoning based on chains of thought (CoTs) enables large language models (LLMs) to solve problems by thinking step by step and becomes the mainstream solution for Question-Answering (QA) tasks. Knowledge graph (KG)-enhanced CoT technology helps correct factual errors or predict reasoning direction. Existing KG-enhanced methods find relevant information in KGs “within” each reasoning step of CoTs. However, in some cases, logical connections “between” reasoning steps may be missing or wrong, leading to broken reasoning chains and wrong reasoning direction. To solve the above problem, we argue that the errors between reasoning steps require collaborative verification and mining of multiple triplets and multiple paths in KG. So we propose the DCMKC (Dual Consistency Matching for KG and CoT) method, aiming to maintain semantic and structural consistency between KG and CoT. The main idea is to convert CoTs and KGs into two granularity-aligned graphs, transforming multi-hop reasoning and KG matching into iterative matching and modification of two graphs. In each iteration, DCMKC matches the KG reasoning chains with CoTs based on semantic similarity and judges the structural consistency between them. Then it modifies CoTs using the matched chains. After iterations, the CoTs and KG reasoning chains reach high semantic and structural consistency, which is theoretically and experimentally demonstrated by kernel and spectral methods. The two kinds of chains are then used to generate the final answers. Experimental results show that our method outperforms baselines on multiple datasets, especially on multi-answer questions, with up to 5.1% improvement over the baseline.

## introduction the question-answering (qa) task is an important benchmark for evaluating the ability of large language models (llms) to understand and process natural language @xcite . we can evaluate the performance of llms in advanced cognitive functions such as natural language understanding, semantic analysis, and knowledge reasoning through the qa task. the qa task with llms has shown broad application prospects, including intelligent customer service @xcite , financial analysis @xcite , medical inquiry @xcite , and other scenarios. early works use knowledge graph (kg)enhanced llms as the core technique for qa tasks. they construct reasoning steps with high accuracy and interpretability by walking on the kg with llms @xcite @xcite @xcite . however, the kg has a complex structure, which makes it difficult for the llms to identify the knowledge and logical relationships in the kg @xcite . to better acquire the key information, subsequent works use the chain of thought (cot) to identify key details, delineate the scope, and offer guidance for graph reasoning @xcite @xcite . however, semantic inconsistency exists because the cot and the kg may describe knowledge differently. it will cause errors when matching and locating knowledge. furthermore, the cot is usually used to locate relevant entities in the kg, failing to use its mechanism to explore the commonsense knowledge and reasoning capability of the llm itself. another type of work is based on cots. they offer examples with intermediate steps @xcite or prompt the llm to think step by step @xcite , enabling the llm to show the reasoning process. this not only improves the interpretability of the answers but also offers a traceable foundation for the model's decision-making process. however, the internal working mechanism of the llm relies on statistical learning and probability prediction @xcite , so the llm sometimes generates plausible text, known as "hallucination" @xcite . to address hallucination, subsequent works introduce the kg. these methods typically use semantic correlation technology to retrieve relevant information from the kg for each step in the cot, then verify and correct the steps and guide the subsequent reasoning @xcite @xcite . the logical relationships between steps in cots sometimes require the topological structure between multiple triplets in kg to verify. the correctness of the long path cot requires verification of complex paths in kg. both of the above situations cannot be solved by a single triplet and require the matching of kg and cot topology structures. figure 1 presents a broken reasoning chain: each step in cot is correct since each corresponds to a triplet a-b/c-d/e-f in the kg, while the whole chain is incorrect since the logical relationships between steps do not exist. this logical structure inconsistency will cause broken reasoning chains and wrong reasoning directions, affecting the coherence and accuracy of the reasoning. to address these shortcomings, we propose the dcmkc (dual consistency matching for kg and cot) method. this method converts the cot into a graph based on the reasoning steps and introduces super nodes into the kg. this converts them into graphs of the same granularity for accurate matching. in each iteration, dcmkc matches the kg reasoning chains with cots based on semantic similarity and judges the structural consistency between them. then it modifies cots using the matched chains. after iterations, the cots and kg reasoning chains reach high semantic and structural consistency, which is theoretically and experimentally demonstrated by kernel and spectral methods. the two kinds of chains are then used to generate the final answers. in summary, our contributions are as follows: ## preliminary the bipartite graph is a graph whose node set can be divided into two disjoint subsets. each edge in the graph connects nodes from two different node sets. the weighted bipartite graph assigns a weight to each edge within the graph. the weight can represent the relationship between nodes from different node sets. here are some concepts about the weighted bipartite graph. the examples are shown in the lower left corner of figure 2 . matching. select a set of edges in the bipartite graph such that no two edges have shared endpoints. this set is referred to as a matching of the bipartite graph. in a matching, two disjoint sets of nodes represent two different types of entities, and edges represent the connection between them. ## conclusion in this paper, we introduce a model based on semantic and structural consistency matching called dcmkc. the method converts the cots into a graph and introduces super nodes into the kg to make the two correspond structurally and semantically, which is theoretically and experimentally demonstrated by kernel and spectral methods. in the reasoning process, the method makes the cots and the kg reasoning chains work together through the framework of iterative interaction and bidirectional modification. experiments show that dcmkc outperforms the baselines on multiple datasets, especially on multi-answer questions, with up to 5.1% improvement.