MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
On the map: LLM reasoning → Knowledge- and tool-augmented reasoning
Abstract (arXiv)
Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation tasks. However, they often suffer from limitations such as difficulty in incorporating new knowledge, generating hallucinations, and explaining their reasoning process. To address these challenges, we propose a novel prompting pipeline, named method, that leverages knowledge graphs (KGs) to enhance LLMs' inference and transparency. Our method enables LLMs to comprehend KG inputs and infer with a combination of implicit and external knowledge. Moreover, our method elicits the mind map of LLMs, which reveals their reasoning pathways based on the ontology of knowledge. We evaluate our method on diverse question & answering tasks, especially in medical domains, and show significant improvements over baselines. We also introduce a new hallucination evaluation benchmark and analyze the effects of different components of our method. Our results demonstrate the effectiveness and robustness of our method in merging knowledge from LLMs and KGs for combined inference. To reproduce our results and extend the framework further, we make our codebase available at https://github.com/wyl-willing/MindMap.
Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 32 of its 41 references that are in the map. First posted on arXiv 17 Aug 2023.
Builds on
32 of 41 references.
Closest ancestors · 2
Same direction, newest first
- 2023Complex Logical Reasoning over Knowledge Graphs using Large Language Models2 May
- 2022GreaseLM: Graph REASoning Enhanced Language Models for Question Answering21 Jan
Other references · 30
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- 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
- 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
- 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
- 2022Survey of Hallucination in Natural Language Generationcited by 2,056
- 2022Large Language Models Encode Clinical Knowledgecited by 1,807
- 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 713
18 more
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- 2019ERNIE: Enhanced Language Representation with Informative Entitiescited by 525
- 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 386
- 2023Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedbackcited by 351
- 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 324
- 2019K-BERT: Enabling Language Representation with Knowledge Graphcited by 303
- 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
- 2021ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generationcited by 258
- 2023GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarkingcited by 209
- 2020A Survey of the State of Explainable AI for Natural Language Processingcited by 206
- 2023Progressive Prompts: Continual Learning for Language Modelscited by 195
- 2022Deep Bidirectional Language-Knowledge Graph Pretrainingcited by 156
- 2023Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answeringcited by 119
- 2020CoLAKE: Contextualized Language and Knowledge Embeddingcited by 86
- 2021Complex Temporal Question Answering on Knowledge Graphscited by 69
- 2018Improving Natural Language Inference Using External Knowledge in the Science Questions Domaincited by 48
- 2023ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examinationcited by 10
- 2020Word2vec Conjecture and A Limitative Resultcited by 1
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Citing papers per year: 2023: 6; 2024: 22; 2025: 32; 2026: 13.
73 papers.
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- 2025Resisting Contextual Interference in RAG via Parametric-Knowledge Reinforcement5 Jun 2025
- 2025Out of Style: RAG's Fragility to Linguistic Variation11 Apr 2025
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- 2025Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities22 Jun 2025
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- 2025COSINT-Agent: A Knowledge-Driven Multimodal Agent for Chinese Open Source Intelligence5 Mar 2025
- 2025RAG-Enhanced Collaborative LLM Agents for Drug Discovery22 Feb 2025
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- 2026FineREX: Fine-Tuned NER-RE for Human Smuggling Knowledge Graphs18 Jun 2026
- 2026Suppressing Domain-Specific Hallucination in Construction LLMs: A Knowledge Graph Foundation for GraphRAG and QLoRA on River and Sediment Control Technical Standards3 Mar 2026
- 2026Personalized Graph-Empowered Large Language Model for Proactive Information Access25 Feb 2026
- 2026CE-GOCD: Central Entity-Guided Graph Optimization for Community Detection to Augment LLM Scientific Question Answering29 Jan 2026
- 2026DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge Graphs28 Jan 2026
- 2025DeepEvidence: Empowering Biomedical Discovery with Deep Knowledge Graph Research23 Dec 2025
- 2025GRAD: Graph-Retrieved Adaptive Decoding for Hallucination Mitigation5 Nov 2025
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