在处理复杂问题和进行深度思考时,传统的线性思维往往难以满足需求。思维图谱提示(Graph of Thought Prompting)作为一种非线性思维工具,通过构建类似于网络图的思维结构,帮助人们更全面、深入地理解和分析问题。本文将探讨思维图谱提示的概念、工作原理、优缺点以及应用场景。 1. 思维图谱提示的工作原理 思维图谱提示是基于...
贡献五:提出了一个新的metric,用于评估prompting 策略。 GOT framework GOT可以被建模为一个元组(,,,G,τ,ε,R), G 是 LLM reasoning process(包含所有LLM thoughts和上下文和依赖关系,)τ是隐藏的 thought transformations,ε是一个评价函数用于观察thoughts的分数,R是一个排序函数用于选择最相关的thoughts. Reason...
Graph of Thoughts (GoT) is a novel framework designed to enhance the prompting capabilities of Large Language Models (LLMs) for complex problem-solving tasks. GoT surpasses existing paradigms like Chain-of-Thought (CoT) and Tree of Thoughts (ToT) by representing the information generated by...
Large Language Model Guided Tree-of-Thought Jieyi Long 2023 Chain of Thought Prompting Elicits Reasoning in Large Language Models Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, E. Chi, F. Xia, Quoc Le, Denny Zhou 2022 Self-Consistency Improves Cha...
由此我们提出一个问题:What hinders the ability of LLMs on graph reasoning tasks? 对于这个问题,论文提出了自己的观点:在图推理过程中将图数据转换成文本描述(Graph2Text)的过程限制了LLMs的表现。(Graph2Text的优点是可以让LLM直接用文本描述处理图数据) 使用Graph2Text后,LLMs需要从文本中辨别出隐藏的graph st...
The final thought states' scores indicate the number of errors in the sorted list. Documentation The paper gives a high-level overview of the framework and its components. In order to understand the framework in more detail, you can read the documentation of the individual modules. ...
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Zero-shot prompting: 只输入问题的描述和输出格式 Few-shot prompting: 输入问题的描述、输出格式和少量的例子 Chain-of-thought (CoT): 给模型一些例子,每个例子都展示了如何一步一步解决问题 Zero-shot CoT prompting (ZERO-COT): 让模型自己一步一步解决,不给提示,"Let's think step by step" ...
• This work aims to align graph domain-specific structural knowl-edge with the reasoning ability of Large Language Models (LLMs)to improve the generalization of graph learning. -这项工作旨在将特定图领域的结构知识与大型语言模型(LLM)的推理能力结合起来,以提高图学习的泛化能力。