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thought...
我们将GraphLLM与以下两类方法进行对比:prompting 和 fine-tuning。 prompting方法有以下策略:zero-shot prompting,few-shot in context learning,few-shot chain-of-thought prompting. fine-tuning方法有以下策略:prefix tuning,LoRA(Low-Rank Adaptation). 由于上下文的长度限制,所有的few-shot方法都采用one-shot,对于...
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. ...
Compositional Chain-of-Thought Prompting for Large Multimodal Models Dysen-VDM: Empowering Dynamics-aware Text-to-Video Diffusion with LLMs The All-Seeing Project V2: Towards General Relation Comprehension of the Open World New dataset and New Task (Relation Conversation) we propose a novel task...
the little girl thought she had lost him. But soon she saw oneofhis ears sticking up through the hole,forthe strong pressureofthe air was keeping him up so that he couldnotfall. She crepttothe hole,caught Toto by the ear,anddragged him into the room again,afterward ...
we introduce a new prompting technique specially designed for graph traversal tasks (PathCompare), which demonstrates a notable increase in the performance of LLMs in comparison to standard prompting techniques such as Chain-of-Thought (CoT) (The code for reproducing the results of this paper can...
Chain-of-Thought (CoT) prompting along with sub-question generation and answering has enhanced multi-step reasoning capabilities of Large Language Models (LLMs). However, prompting the LLMs to directly generate sub-questions is suboptimal since they sometimes generate redundant or irrelevant questions....
finding that lower text chunk sizes will extract more information. i also thought it would be interesting to inspect the node degree distributions of the constructed graph. the following code retrieves and visualizes node degree distributions: degree_dist = graph.query( """ match (e:__entity_...
Thought:IshouldcallSearch()toseethecurrentscoreofthegame.Act:Search("What is the current score of game X?")Observation:Thecurrentscoreis24-21...(repeatNtimes) 这种方法利用了Chain-of-Thought链式思考提示,每一步只做一个行动选择。虽然这对简单任务可能有效,但也有几个主要缺点: ...