Knowledge Tree
Think-on-Graph
Also known as: ToG, Think-on-graph 2.0
Facts (15)
Sources
Daily Papers - Hugging Face huggingface.co 6 facts
claimIn certain scenarios, the performance of the 'Think-on-Graph' (ToG) approach using small large language models can exceed that of large models like GPT-4, thereby reducing the cost of LLM deployment and application.
measurementThe 'Think-on-Graph' (ToG) approach achieves overall state-of-the-art performance in 6 out of 9 datasets, functioning as a training-free method with lower computational cost and better generality compared to previous state-of-the-art methods that rely on additional training.
claimThe 'Think-on-Graph' (ToG) approach provides a flexible plug-and-play framework for different large language models, knowledge graphs, and prompting strategies without requiring additional training costs.
procedureThe 'Think-on-Graph' (ToG) approach implements the 'LLMotimesKG' paradigm by having an LLM agent iteratively execute beam search on a knowledge graph to discover promising reasoning paths and return likely reasoning results.
claimCompared with standard large language models, the 'Think-on-Graph' (ToG) approach demonstrates better deep reasoning power.
claimThe 'Think-on-Graph' (ToG) approach enables knowledge traceability and knowledge correctability by leveraging LLM reasoning and expert feedback.
Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org 3 facts
referenceThink-on-Graph (Sun et al., 2023) treats the LLM as an agent that iteratively executes beam search on a knowledge graph, discovering and evaluating reasoning paths. This agent-based framing reflects a move toward interpretable, step-wise reasoning akin to human problem-solving.
referenceThe paper 'Think-on-graph 2.0: deep and interpretable large language model reasoning with knowledge graph-guided retrieval' explores the integration of large language model reasoning with knowledge graph-guided retrieval.
claimGraphRAG, KG-RAG, ToG, ToG2.0, and FMEA-RAG incorporate structured graph reasoning and multi-hop retrieval into the RAG framework, allowing large language models to reason over graph-structured evidence for tasks such as industrial fault diagnosis, knowledge-based summarization, and domain-specific decision making.
Grounding LLM Reasoning with Knowledge Graphs - arXiv arxiv.org Dec 4, 2025 2 facts
procedureThe automatic graph exploration method utilizes a multi-step Search + Prune pipeline, inspired by the 'think-on-graph' process, which involves retrieving and pruning relation types using LLM guidance, followed by discovering and filtering neighboring entities.
claimSystems such as Think-on-Graph, MindMap, RoG, KG-GPT, and Li et al. (2025) demonstrate improved reasoning performance through graph-based scaffolding.
Empowering GraphRAG with Knowledge Filtering and Integration arxiv.org Mar 18, 2025 2 facts
referenceMavromatis and Karypis (2024) authored 'Think-on-graph 2.0: Deep and interpretable large language model reasoning with knowledge graph-guided retrieval', published in arXiv e-prints.
referenceSun et al. authored 'Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph', published in The Twelfth International Conference on Learning Representations.
Large Language Models Meet Knowledge Graphs for Question ... arxiv.org Sep 22, 2025 1 fact
referenceThe ToG method, proposed by Sun et al. in 2024, uses beam-search-based retrieval and LLM agents with GPT-3.5-Turbo, GPT-4, and Llama-2-70B-Chat models to perform KBQA and open-domain QA tasks, evaluated using Hits@1 on CWQ, WQSP, GrailQA, QALD10-en, and WQ datasets.
Knowledge Graph Combined with Retrieval-Augmented Generation ... drpress.org Dec 2, 2025 1 fact
referenceMa et al. introduced 'Think-on-graph 2.0', a method for deep and interpretable LLM reasoning using knowledge graph-guided retrieval, in an arXiv preprint in 2024.