Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Language models are increasingly being deployed for general problem solving
across a wide range of tasks, but are still confined to token-level,
left-to-right decision-making processes during inference. This means they can
fall short in tasks that require exploration, strategic lookahead, or where
initial decisions play a pivotal role. To surmount these challenges, we
introduce a new framework for language model inference, Tree of Thoughts (ToT),
which generalizes over the popular Chain of Thought approach to prompting
language models, and enables exploration over coherent units of text (thoughts)
that serve as intermediate steps toward problem solving. ToT allows LMs to
perform deliberate decision making by considering multiple different reasoning
paths and self-evaluating choices to decide the next course of action, as well
as looking ahead or backtracking when necessary to make global choices. Our
experiments show that ToT significantly enhances language models'
problem-solving abilities on three novel tasks requiring non-trivial planning
or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in
Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of
tasks, our method achieved a success rate of 74%. Code repo with all prompts:
https://github.com/ysymyth/tree-of-thought-llm.
6466f83f1343dce20e596a68
Shunyu Yao
admin_assigned
2023-05-22T14:57:27.314Z
62aa3b8f46d66bafe2f55899
/avatars/d45c6406042c3f6167153aba76d03776.svg
Shunyu Yao
user
ysymyth
6466f83f1343dce20e596a69
Dian Yu
6466f83f1343dce20e596a6a
Jeffrey Zhao
6466f83f1343dce20e596a6b
Izhak Shafran
6466f83f1343dce20e596a6c
Thomas L. Griffiths
6466f83f1343dce20e596a6d
Yuan Cao
claimed_verified
2023-06-01T05:54:09.980Z
6433a8c37b8247480105cf7f
/avatars/a19536f3f3b8eae3609648bea112b381.svg
Yuan Cao
user
caoyuan33
6466f83f1343dce20e596a6e
Karthik Narasimhan
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Language models are increasingly being deployed for general problem solving
across a wide range of tasks, but are still confined to token-level,
left-to-right decision-making processes during inference. This means they can
fall short in tasks that require exploration, strategic lookahead, or where
initial decisions play a pivotal role. To surmount these challenges, we
introduce a new framework for language model inference, Tree of Thoughts (ToT),
which generalizes over the popular Chain of Thought approach to prompting
language models, and enables exploration over coherent units of text (thoughts)
that serve as intermediate steps toward problem solving. ToT allows LMs to
perform deliberate decision making by considering multiple different reasoning
paths and self-evaluating choices to decide the next course of action, as well
as looking ahead or backtracking when necessary to make global choices. Our
experiments show that ToT significantly enhances language models'
problem-solving abilities on three novel tasks requiring non-trivial planning
or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in
Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of
tasks, our method achieved a success rate of 74%. Code repo with all prompts:
https://github.com/ysymyth/tree-of-thought-llm.
The Tree of Thoughts framework enhances language models' problem-solving capabilities by enabling the exploration and evaluation of multiple reasoning paths and thoughts.
Tree of Thoughts Chain of Thought language model inference problem solving Game of 24 Creative Writing Mini Crosswords deliberate decision making self-evaluation reasoning paths lookahead backtracking global choices