Knowledge Tree
Graph of Thoughts
Also known as: GoT, Graph of Thoughts, Graph-of-Thought
Facts (20)
Sources
Grounding LLM Reasoning with Knowledge Graphs - arXiv arxiv.org Dec 4, 2025 18 facts
procedureThe Graph-of-Thought (GoT) framework uses breadth-first traversal to retain a fixed number of thoughts at each depth, evaluating them using either Selection- or Score-based strategies.
claimLarge Language Models (LLMs) struggle to effectively merge divergent results from multiple branches in the Graph of Thought (GoT) reasoning strategy, specifically failing to combine different triples found in separate branches during graph exploration.
procedureIn the Graph-of-Thought (GoT) framework, new thoughts are generated from an initial thought and added to the graph, with merge operations integrating two thought chains into a single coherent reasoning step represented as a new node with edges from both parents.
claimTree of Thoughts (ToT) and Graph of Thoughts (GoT) reasoning methods exhibit exponential growth in computational complexity due to their branching structures.
claimTree of Thoughts (ToT) and Graph of Thoughts (GoT) reasoning strategies exhibit more 'answer found but not returned' error cases than Chain of Thought (CoT), suggesting better retrieval capabilities but occasional failures in synthesis.
claimThe framework proposed in 'Grounding LLM Reasoning with Knowledge Graphs' incorporates multiple reasoning strategies, specifically Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT).
perspectiveThe higher computational complexity of Graph-of-Thought (GoT) does not consistently translate to improved accuracy compared to Tree-of-Thought (ToT), suggesting diminishing returns for the increased cost.
claimThe framework evaluates three reasoning strategies: Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT).
procedureThe experimental implementation extends the Agent and Automatic Graph Exploration methods with three reasoning strategies during inference: Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT).
claimThe heuristic voting mechanism used in the Graph-of-Thought (GoT) framework prompts the LLM to estimate the probability of the current state solving the given input question, allowing the model to evaluate multiple candidate reasoning paths.
claimGraph-of-Thought (GoT) reasoning amplifies computational cost by incorporating aggregation transformations that attempt to merge every pair of thoughts at each depth, leading to an additional cost proportional to the square of the number of thoughts.
procedureThe framework for grounding LLM reasoning in knowledge graphs integrates each reasoning step with structured graph retrieval and combines strategies like Chain of Thought (CoT), Tree of Thoughts (ToT), and Graph of Thoughts (GoT) with adaptive graph search.
referenceThe Graph-of-Thought (GoT) reasoning framework organizes reasoning into a directed graph structure where each node represents a thought and edges represent dependencies between thoughts.
claimThe Graph of Thought (GoT) reasoning strategy did not outperform the Tree of Thought (ToT) strategy in the reported experiments.
claimThe Graph of Thoughts (GoT) strategy did not significantly outperform the Tree of Thoughts (ToT) strategy, suggesting that merging divergent reasoning paths remains a challenging intervention design problem.
procedureFor Tree-of-Thought (ToT) and Graph-of-Thought (GoT) reasoning strategies, the evaluation includes the impact of stepwise decision-making using two State Evaluation methods: Selection and Score.
procedureThe method in 'Grounding LLM Reasoning with Knowledge Graphs' combines reasoning strategies (Chain-of-Thought, Tree-of-Thought, Graph-of-Thought) with two graph interaction methods: an agent to navigate the graph, and an automatic graph exploration mechanism based on generated text.
claimGraph of Thoughts (GoT) increases the total number of evaluations required for reasoning because it allows merges between reasoning paths.
KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org May 20, 2024 2 facts
claimPrompt engineering techniques, including Chain of Thought (CoT), Tree of Thought (ToT), Graph of Thoughts (GoT), and ReAct (Reason and Act), have demonstrated significant improvements in the reasoning abilities and task-specific actions of Large Language Models.
claimPrompt engineering techniques, including Chain of Thought (CoT), Tree of Thought (ToT), Graph of Thoughts (GoT), and ReAct (Reason and Act), have demonstrated significant improvements in the reasoning abilities and task-specific actions of Large Language Models.