Large Language Models ↔ Tree of Thoughts

Relations (1)

related 0.80 — strongly supporting 8 facts

Tree of Thoughts (ToT) is a prompting technique specifically designed for Large Language Models (LLMs), extending Chain-of-Thought by enabling LLMs to explore multiple reasoning paths in a tree structure [1][2]. It improves LLMs' reasoning abilities alongside other prompt engineering methods [3][4], and was introduced for deliberate problem-solving with LLMs [5][6].

Facts (8)

Sources

The Synergy of Symbolic and Connectionist AI in LLM ... arxiv.org arXiv 2 facts

claimTree-of-Thought (ToT) prompting allows LLMs to explore multiple reasoning paths simultaneously in a tree structure.

claimChain-of-Thought (CoT) and Tree-of-Thoughts (ToT) reasoning mechanisms mitigate the limitations of token-level constraints in Large Language Models (LLMs).

The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org arXiv 2 facts

referenceShunyu Yao et al. introduced the 'Tree of Thoughts' framework, which enables deliberate problem solving using large language models.

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.

KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org arXiv 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.

Building Trustworthy NeuroSymbolic AI Systems - arXiv arxiv.org arXiv 2 facts

referenceYao et al. (2023a) authored the paper titled 'Tree of thoughts: Deliberate problem solving with large language models', published as arXiv:2305.10601.

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).