Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
(2311.04254)Abstract
Recent advancements in 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. We evaluate XoT on several challenging multi-solution problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate that XoT significantly outperforms existing approaches. Notably, XoT can yield multiple solutions with just one LLM call, showcasing its remarkable proficiency in addressing complex problems across diverse domains.
Overview
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Introduces 'Everything of Thoughts' (XOT), a novel approach for enhancing LLMs through thought generation, challenging the conventional limitations of performance, efficiency, and flexibility.
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XOT combines Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) with a lightweight network to improve the thought generation process and integrate extensive domain knowledge into LLMs.
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Demonstrates superior problem-solving across several tasks, outperforming existing methods by generating high-quality thoughts with fewer LLM interactions.
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Suggests theoretical and practical implications of XOT for the future of AI and machine learning in complex decision-making scenarios and opens up avenues for further research.
Exploring "Everything of Thoughts": A New Paradigm in Thought Generation for LLMs
Introduction to Everything of Thoughts (XOT)
Recent advances in artificial intelligence have brought to the forefront the impressive problem-solving capabilities of LLMs. Among the myriad of techniques devised to enhance their reasoning, the decomposition of complex queries into intermediate steps, or "thoughts", stands out. This approach, however, faces a complexity conundrum best represented by the "Penrose Triangle" - striving for performance, flexibility, and efficiency often seems like a pursuit of an impossible trinity. In this context, the paper introduces "Everything of Thoughts" (XOT), a novel thought prompting method that challenges this notion and emerges as a comprehensive thought generation framework enhancing LLMs' capabilities beyond current boundaries.
Methodological Insight
At the core of XOT lies a synergistic integration of Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS), coupled with a lightweight network, designed to pretrain and specialize in thought search patterns. This framework not only assimilates extensive domain knowledge into the LLM but also significantly streamlines the thought generation process through its MCTS-LLM collaborative thought revision framework.
Key characteristics of XOT encompass:
- High Performance through the incorporation of external domain knowledge, enabling the LLM to navigate complex problems with unprecedented accuracy.
- Efficiency, by minimizing LLM interactions through autonomous, high-quality thought production, thereby curtailing computational demand.
- Flexibility in thought topology, allowing for an unconstrained exploration of problem-solving paths mirroring the complex cognition processes found in human thought.
Evaluation and Insights
XOT's efficacy was rigorously tested across a suite of challenging problem-solving tasks - Game of 24, 8-Puzzle, and Pocket Cube. The results are compelling, showcasing XOT's superior problem-solving prowess across varied domains, evidenced by significant improvements in performance metrics over existing paradigms. Specifically, XOT demonstrated its ability to efficiently and flexibly generate comprehensive cognitive mappings, addressing complex problems with fewer LLM calls and outperforming traditional methods like Chain-of-Thought (CoT), Self-Consistency CoT (CoT-SC), and Graph-of-Thought (GoT).
Theoretical and Practical Implications
The development of XOT potentially marks a watershed moment in the field of generative AI and problem solving with LLMs. Theoretically, it extends our understanding of LLM interactions and cognitive mapping, demonstrating the feasibility of achieving high performance, efficiency, and flexibility in a unified framework. Practically, XOT paves the way for more sophisticated applications of LLMs in complex decision-making scenarios, ranging from optimization problems to strategic game-playing and beyond.
Looking Ahead
While XOT's current implementation and results are promising, its framework opens avenues for future exploration and refinement. Specifically, investigating the portability of XOT to tasks with less defined objectives or those requiring multi-agent collaboration could further elucidate its potential. Additionally, optimizing the training efficiency of the necessary policy and value networks, particularly in more ambiguous problem settings, remains an area ripe for research.
Conclusion
"Everything of Thoughts" defies the traditional constraints of thought generation paradigms for LLMs, setting a new benchmark for performance, efficiency, and flexibility. As we delve deeper into the era of AI and machine learning, XOT's innovative approach fosters a more profound and nuanced exploration of the capabilities of LLMs, heralding a future where AI's problem-solving abilities are limited only by the breadth of our imagination.