思维图增强：由强化学习驱动的LLM自适应提示方法
Reinforced Graph of Thoughts: RL-Driven Adaptive Prompting for LLMs
AI总结：
本文提出Reinforced Graph of Thoughts (RGoT)，通过强化学习自动生成适应任务复杂度的思维图结构，提升大型语言模型的提示效果。
摘要：
Graph of Thoughts (GoT)，作为一种针对大型语言模型（LLMs）的通用提示范式，已被证明在复杂问题解决中具有用处。通过执行一系列操作的图，LLM的思维被结构化为任意图，形成实际的思维图。最初，操作图是手动定义的，需要深入了解问题的解决方案。这种静态的操作图缺乏适应性。我们提出Reinforced Graph of Thoughts (RGoT)，一种利用强化学习（RL）自动从人类定义的集合中生成操作图的自动化方法。结果表明，在某些约束下，可以以自动化的方式构建适应任务复杂度的操作图。
英文摘要：
Graph of Thoughts (GoT), a generalized form of recent prompting paradigms for large language models (LLMs), has been shown to be useful for elaborate problem solving. By executing a graph of operations, thoughts of the LLM are structured as an arbitrary graph, forming the actual graph of thoughts. Originally, the graph of operations is defined manually, which requires in-depth knowledge about the solution of the problem to solve. Such a static graph of operations is rigid and therefore lacks adaptability. We propose Reinforced Graph of Thoughts (RGoT), an automated approach to the GoT prompting paradigm that leverages reinforcement learning (RL) to adaptively generate a graph of operations from a human-defined set. Results indicate that, under certain constraints, it is possible to construct graphs of operations adaptively to the task's complexity in an automated way.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
