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Grounding LLM Reasoning with Knowledge Graphs - arXiv arxiv.org 9 facts
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).
claimTree-of-Thought (ToT) generalizes Chain-of-Thought by modeling the reasoning process as a tree, enabling simultaneous exploration of multiple reasoning paths.
claimThe framework evaluates three reasoning strategies: Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT).
measurementThe Tree of Thought (ToT) reasoning strategy achieved performance improvements of 54.74% in agent performance and 11.74% in exploration mode compared to the Chain of Thought (CoT) baseline.
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).
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.
claimIn the Tree of Thoughts (ToT) reasoning strategy, performance shows a slight upward trend as tree width increases, with a more pronounced performance difference observed when moving from one branch to two branches compared to Chain of Thought (CoT).
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.
The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org 3 facts
claimLLM-based Agentic Architectures (LAAs) utilize advanced reasoning mechanisms such as Chain-of-Thought (CoT) and Tree-of-Thoughts (ToT) to solve complex problems by analogizing human reasoning steps.
claimTree-of-Thought (ToT) prompting extends the Chain-of-Thought approach by allowing large language models to explore multiple reasoning paths simultaneously within a tree structure.
claimAutomating code generation, optimizing hybrid Program-of-Thought (PoT)/Chain-of-Thought (CoT)/Tree-of-Thought (ToT) models, incorporating self-verification and self-correction, and adopting PoT into domain-specific applications like logical deduction and scientific discovery can significantly advance the capabilities of LLM-empowered Autonomous Agents.
KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org 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.
Building Trustworthy NeuroSymbolic AI Systems - arXiv arxiv.org 1 fact
claimMethods like chain of thoughts and tree of thoughts prompting can act as sanity checks to examine the deceptive nature of Large Language Models (Connor Leahy 2023; Yao et al. 2023a).
The Synergy of Symbolic and Connectionist AI in LLM ... arxiv.org 1 fact
claimChain-of-Thought (CoT) and Tree-of-Thoughts (ToT) reasoning mechanisms mitigate the limitations of token-level constraints in Large Language Models (LLMs).