KG-o1: Enhancing Multi-hop Question Answering in Large Language Models via Knowledge Graph Integration

• URL: http://arxiv.org/abs/2508.15790v1
• Date: Tue, 12 Aug 2025 04:29:10 GMT
• Title: KG-o1: Enhancing Multi-hop Question Answering in Large Language Models via Knowledge Graph Integration
• Authors: Nan Wang, Yongqi Fan, yansha zhu, ZongYu Wang, Xuezhi Cao, Xinyan He, Haiyun Jiang, Tong Ruan, Jingping Liu,
• Abstract summary: KG-o1 is a four-stage approach that integrates knowledge graphs to enhance the multi-hop reasoning abilities of Large Language Models.<n>We conduct experiments on two simple and two complex datasets.<n>The results show that KG-o1 models exhibit superior performance across all tasks compared to existing LRMs.
• Score: 29.320693000484273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) face challenges in knowledge-intensive reasoning tasks like classic multi-hop question and answering, which involves reasoning across multiple facts. This difficulty arises because the chain of thoughts (CoTs) generated by LLMs in such tasks often deviate from real or a priori reasoning paths. In contrast, knowledge graphs (KGs) explicitly represent the logical connections between facts through entities and relationships. This reflects a significant gap. Meanwhile, large reasoning models (LRMs), such as o1, have demonstrated that long-step reasoning significantly enhances the performance of LLMs. Building on these insights, we propose KG-o1, a four-stage approach that integrates KGs to enhance the multi-hop reasoning abilities of LLMs. We first filter out initial entities and generate complex subgraphs. Secondly, we construct logical paths for subgraphs and then use knowledge graphs to build a dataset with a complex and extended brainstorming process, which trains LLMs to imitate long-term reasoning. Finally, we employ rejection sampling to generate a self-improving corpus for direct preference optimization (DPO), further refining the LLMs reasoning abilities. We conducted experiments on two simple and two complex datasets. The results show that KG-o1 models exhibit superior performance across all tasks compared to existing LRMs.

Related papers

• ProgRAG: Hallucination-Resistant Progressive Retrieval and Reasoning over Knowledge Graphs [2.9539912037183362]
  Large Language Models (LLMs) demonstrate strong reasoning capabilities but struggle with hallucinations and limited transparency.<n>We propose ProgRAG, a multi-hop knowledge graph question answering (KGQA) framework that decomposes complex questions into sub-questions and extends partial reasoning paths.<n> Experiments on three well-known datasets demonstrate that ProgRAG outperforms existing baselines in multi-hop KGQA.
  arXiv  Detail & Related papers  (2025-11-13T12:14:36Z)
• GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models [59.72897499248909]
  We propose a novel graph retriever trained end-to-end with Large Language Models (LLMs)<n>Within the extracted subgraph, structural knowledge and semantic features are encoded via soft tokens and the verbalized graph, respectively, which are infused into the LLM together.<n>Our approach consistently achieves state-of-the-art performance, validating the strength of joint graph-LLM optimization for complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-09-20T02:38:00Z)
• Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers [74.17516978246152]
  Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques.<n>We propose EXSEARCH, an agentic search framework, where the LLM learns to retrieve useful information as the reasoning unfolds.<n>Experiments on four knowledge-intensive benchmarks show that EXSEARCH substantially outperforms baselines.
  arXiv  Detail & Related papers  (2025-05-26T15:27:55Z)
• LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph [57.382255728234064]
  Large Language Models (LLMs) have impressive capabilities in text understanding and zero-shot reasoning.<n> Knowledge Graphs (KGs) provide rich and reliable contextual information for the reasoning process of LLMs.<n>We propose a novel Lightweight and efficient Prompt learning-ReasOning Framework for KGQA (LightPROF)
  arXiv  Detail & Related papers  (2025-04-04T03:03:47Z)
• Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning [73.2950349728376]
  Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks.<n>However, they still encounter challenges in reasoning tasks that require understanding and inferring relationships between pieces of information.<n>This challenge is particularly pronounced in tasks involving multi-step processes, such as logical reasoning and multi-hop question answering.<n>We propose Reasoning with Graphs (RwG) by first constructing explicit graphs from the context.
  arXiv  Detail & Related papers  (2025-01-14T05:18:20Z)
• Thinking with Knowledge Graphs: Enhancing LLM Reasoning Through Structured Data [0.9284740716447338]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation.<n>Recent research has shown promising results in leveraging knowledge graphs (KGs) to enhance LLM performance.<n>We have developed different techniques that tightly integrate KG structures and semantics into LLM representations.
  arXiv  Detail & Related papers  (2024-12-14T02:51:47Z)
• Dual Reasoning: A GNN-LLM Collaborative Framework for Knowledge Graph Question Answering [38.31983923708175]
  We propose Dual-Reasoning, a novel framework that integrates an external system based on Graph Neural Network (GNN) for explicit reasoning on Knowledge Graphs (KGs)<n>We show that DualR achieves state-of-the-art performance while maintaining high efficiency and interpretability.
  arXiv  Detail & Related papers  (2024-06-03T09:38:28Z)
• FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering [46.41364317172677]
  Large Language Models (LLMs) are often challenged by generating erroneous or hallucinated responses.<n>We propose a unified framework, FiDeLiS, designed to improve the factuality of LLM responses by anchoring answers to verifiable reasoning steps retrieved from Knowledge Graphs.<n>Our method, as a training-free framework, not only improve the performance but also enhance the factuality and interpretability across different benchmarks.
  arXiv  Detail & Related papers  (2024-05-22T17:56:53Z)
• Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning [89.89857766491475]
  We propose a curriculum-based logical-aware instruction tuning framework, named LACT.<n>Specifically, we augment the arbitrary first-order logical queries via binary tree decomposition.<n> Experiments across widely used datasets demonstrate that LACT has substantial improvements(brings an average +5.5% MRR score) over advanced methods, achieving the new state-of-the-art.
  arXiv  Detail & Related papers  (2024-05-02T18:12:08Z)
• Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs [52.42505579545893]
  Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought explanations alongside answers. We propose a novel discriminative and generative CoT evaluation paradigm to assess LLMs' knowledge of reasoning and the accuracy of the generated CoT.
  arXiv  Detail & Related papers  (2024-02-17T05:22:56Z)
• Complex Logical Reasoning over Knowledge Graphs using Large Language Models [13.594992599230277]
  Reasoning over knowledge graphs (KGs) is a challenging task that requires a deep understanding of the relationships between entities. Current approaches rely on learning geometries to embed entities in vector space for logical query operations. We propose a novel decoupled approach, Language-guided Abstract Reasoning over Knowledge graphs (LARK), that formulates complex KG reasoning as a combination of contextual KG search and logical query reasoning.
  arXiv  Detail & Related papers  (2023-05-02T02:21:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.