The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
Open accessAbstract
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 MindMap, 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.
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In the citing paper
“Semantic similarity metrics such as BERTScore [132] and domainspecific variants using ClinicalBERT [133] or SciBERT embeddings assess meaning preservation beyond surface forms [65,67,70,90,111].”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [111]
In the citing paper
“Custom and task-specific metrics emerged frequently, including Hallucination Quantification [111], Medical Algorithm Presentation Criteria(MAPC) [89], Diagnosis Confidence Scoring(DCS) [71], LExT scores combining plausibility and faithfulness [80], Reasoning Path Quality Assessment(RPQA) rated by physicians [87], and LLMw-Ref for reasoning trajectory evaluation [82].”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [111]
In the citing paper
“Safety applications leveraged explanations for several closely related but distinct verification goals: detecting model-generated content unsupported by the input or retrieval context ( hallucination detection ) [111]; flagging outputs that contradict authoritative medical knowledge, irrespective of whether the content was fabricated or faithfully reproduced from training data ( misinformation”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [111]
In the citing paper
“Integration with external knowledge sources, including clinical guidelines [63] and medical knowledge graphs [111], has emerged as a strategy to ground model outputs in verifiable medical knowledge.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [111]
Citations
Cites
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3352100 · Cited by 1,376
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Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3703155 · Cited by 4,688
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3352100 · Cited by 1,376
Graph Retrieval-Augmented Generation: A Survey
Peng, B., Zhu, Y., Liu, Y., Bo, X., Shi, H., Hong, C., Zhang, Y., & Tang, S. (2026) · ACM Transactions on Information Systems · doi:10.1145/3777378 · Cited by 206
On the Evolution of Knowledge Graphs: A Survey and Perspective
Jiang, X., Xu, C., Shen, Y., Sun, X., Tang, L., Wang, S., Chen, Z., Wang, Y., & Guo, J. (2023) · arXiv · doi:10.48550/arxiv.2310.04835 · Cited by 42
Explainability of decoder-only clinical large language models: A scoping review
Mishra, N., Abu-Hanna, A., & Calixto, I. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103501
Metadata
- Authors
- Wen, Y., Wang, Z., & Sun, J.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 10370-10388
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402671983
- Cited by
- 113
- References
- 1
- Citations by year
- 2026: 42 · 2025: 46 · 2024: 25
- FWCI
- 33.35
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- knowledge graphs, large language models, prompting, hallucinations, reasoning transparency, question answering, medical question answering, knowledge fusion
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