Graph

We present the first comprehensive analysis of how the order of graph descriptions impacts LLM performance, evaluating four graph description orders across six graph problems using six mainstream LLMs.

Notes from the workshop given by Google’s Graph Mining team at NeurIPS 2020.

This paper explores how encoding global and local graph structure in different modalities affects LLM performance, with a focus on node classification.

Drawing on the properties of graph structure, the authors propose GraphText, a framework that maps graphs into text space.

This is the first attempt to use large language models to solve graph problems. The authors introduce NLGraph, a natural-language benchmark containing over a thousand basic graph problems, and draw several conclusions from experiments on GPT-3/4. At the end of the paper they propose two methods for improving how LLMs handle graph problems.

Despite the remarkable progress of automated reasoning over natural text, graph reasoning with large language models (LLMs) remains unsatisfactory. In this paper the authors carry out the first comprehensive study of encoding graph-structured data as text so that LLMs can reason over it.