维克多:Tree of Thoughts: Deliberate Problem Solving with Large Language Models阅读笔记4 赞同 · 0 评论文章 模型细节 定义 形式上,GoT 可以建模为一个元组 (G, T , E, R),其中 G 是“LLM 推理过程”(即上下文中的所有 LLM 思想及其关系),T 是潜在的思想转换,E是用于获得思想分数的评估函数,R是用于...
现有的大型语言模型在解决复杂问题时,受限于提示策略的简单性,如直接输入输出(IO)、链式思考(Chain-of-Thought, CoT)和思想树(Tree of Thoughts, ToT)。这些方法在处理需要多步骤推理和信息整合的问题时效果不佳。GoT框架通过将LLM的推理过程建模为一个任意图结构,其中信息单元(“LLM thoughts”)作为顶点,顶点之间...
Discover how Graph of Thoughts aims to revolutionize prompt engineering, and LLMs more broadly, enabling more flexible and human-like problem-solving.
We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is the...
Official Implementation of "Graph of Thoughts: Solving Elaborate Problems with Large Language Models" - spcl/graph-of-thoughts
We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is the ability to model the information gener...
表格1展示了在不使用外部知识的情况下,各种提示方法在三个数据集上的答案级别的精确匹配(EM),令牌级别的 F1,精确度,和召回率。结果显示,作者的方法在所有数据集上都取得了最好的性能,相比于 Chain-of-Thoughts 提示方法,分别在 2WikiMultihopQA,MuSiQue,和 Bamboogle 上提高了 11.4%,8.8%,和 7% 的 EM。
MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models This is the official codebase of the MindMap ❄️ framework for eliciting the graph-of-thoughts reasoning capability in LLMs, proposed in MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large La...
Jiang, X., Zhang, R., Xu, Y., et al.: HyKGE: a hypothesis knowledge graph enhanced framework for accurate and reliable medical LLMs responses. arXiv:2312.15883 (2023) Wen, Y., Wang, Z., Sun, J.: Mindmap: knowledge graph prompting sparks graph of thoughts in large language models....
如KAPING方法通过匹配问题中的实体从知识图中检索相关三元组,增强零样本问答性能;KG增强的推理(KG-Augmented Reasoning), 将复杂的多步任务分解为可管理的子查询,使用一系列中间推理步骤提高LLM的复杂推理能力。Chain of Thought(CoT)和Tree of Thoughts(ToT)等方法模仿人类的逐步推理过程,帮助理解和调试模型的推理...