Everything of Thoughts: Defying the Law of Penrose Triangle for Thought
  Generation
Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions.
654c3f255255ee867109d7a5
Ruomeng Ding
admin_assigned
2023-11-09T09:41:50.061Z
654875f69295970f87ba1600
/avatars/4c387aa53053f88444e7d8fffd6161c1.svg
Ruomeng Ding
user
Czardas
654c3f255255ee867109d7a6
Chaoyun Zhang
claimed_verified
2023-11-10T07:58:22.464Z
654dbac9938fbf1e696be8aa
/avatars/b3c4035c48169c1bfb04a439fce3499f.svg
Chaoyun Zhang
user
vyokky
654c3f255255ee867109d7a7
Lu Wang
654c3f255255ee867109d7a8
Yong Xu
654c3f255255ee867109d7a9
Minghua Ma
admin_assigned
2023-11-09T09:42:56.598Z
653b52098abd634b83f4fd38
/avatars/c9c5023b030e94c1b1abfb7a1c1dfaf3.svg
MingHua Ma
user
Gezelligheid520
654c3f255255ee867109d7aa
Wei Zhang
654c3f255255ee867109d7ab
Si Qin
654c3f255255ee867109d7ac
Saravan Rajmohan
654c3f255255ee867109d7ad
Qingwei Lin
654c3f255255ee867109d7ae
Dongmei Zhang
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought
  Generation
Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions.
XoT, combining pretrained reinforcement learning and Monte Carlo Tree Search, enhances Large Language Models' capabilities for flexible, efficient problem-solving with minimal interactions.
Large Language Models thoughts performance efficiency flexibility Penrose triangle Everything of Thoughts (XoT) pretrained reinforcement learning Monte Carlo Tree Search (MCTS) external domain knowledge MCTS-LLM collaborative thought revision framework cognitive mappings