Prompting techniques like chain of thought (CoT) and tree of thought (ToT) have drastically improved the problem-solving capabilities of large language models (LLMs). However, they assume linear reasoning, in contrast to the non-linear patterns characteristic of human reasoning. A new approach, called graph-of-thought reasoning (GOTR), models reasoning processes as a graph structure that captures non-sequential human thinking, leading to better reasoning capabilities. Conversely, another method, Graph of Thought (GoT) prompting, models each thought generated by an LLM as a node within a graph, using vertices to represent thought dependencies. Despite its benefits, these approaches may be more complex and costly, providing notable improvements primarily for specific types of problems.
Graph-Based Prompting and Reasoning with Language Models
Introduction
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