mlsanityData to 7 Oct 2026

Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph

arXiv 2501.14300 ↗First posted 24 Jan 2025cs.AI cs.CL cs.LGcited by 6

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

also in 1 more
Abstract (arXiv)

Graph Retrieval Augmented Generation (GRAG) is a novel paradigm that takes the naive RAG system a step further by integrating graph information, such as knowledge graph (KGs), into large-scale language models (LLMs) to mitigate hallucination. However, existing GRAG still encounter limitations: 1) simple paradigms usually fail with the complex problems due to the narrow and shallow correlations capture from KGs 2) methods of strong coupling with KGs tend to be high computation cost and time consuming if the graph is dense. In this paper, we propose the Fast Think-on-Graph (FastToG), an innovative paradigm for enabling LLMs to think ``community by community" within KGs. To do this, FastToG employs community detection for deeper correlation capture and two stages community pruning - coarse and fine pruning for faster retrieval. Furthermore, we also develop two Community-to-Text methods to convert the graph structure of communities into textual form for better understanding by LLMs. Experimental results demonstrate the effectiveness of FastToG, showcasing higher accuracy, faster reasoning, and better explainability compared to the previous works.

Key facts. Cited by 6 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 19 of its 39 references that are in the map. First posted on arXiv 24 Jan 2025.

Builds on

19 of 39 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec
  2. 2023Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph15 Jul

Other references · 17

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  3. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  4. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  5. 2019ERNIE: Enhanced Language Representation with Informative Entitiescited by 525
  6. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 339
  7. 2020LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentioncited by 326
  8. 2020K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapterscited by 318
  9. 2022UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Modelscited by 231
  10. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 144
  11. 2023Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sourcescited by 121
  12. 2023Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answeringcited by 119
5 more
  1. 2021CREAK: A Dataset for Commonsense Reasoning over Entity Knowledgecited by 88
  2. 2023RET-LLM: Towards a General Read-Write Memory for Large Language Modelscited by 64
  3. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  4. 2022Knowledge Prompting in Pre-trained Language Model for Natural Language Understandingcited by 9
  5. 2023Towards Resolving Word Ambiguity with Word Embeddingscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

  1. 2025Deliberation on Priors: Trustworthy Reasoning of Large Language Models on Knowledge Graphscited by 6
  2. 2025Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learningcited by 3
  3. 2025Towards Open-World Retrieval-Augmented Generation on Knowledge Graph: A Multi-Agent Collaboration Frameworkcited by 2
  4. 2025PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generationcited by 2
  5. 2025The Role of Exploration Modules in Small Language Models for Knowledge Graph Question Answeringcited by 1
  6. 2026FARE: Fast-Slow Agentic Robotic Explorationcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation
  2. 2024Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
  3. 2025GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models
  4. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
  5. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
  6. 2024Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
  7. 2025AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMs
  8. 2025When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation