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To evaluate and enhance the graph understanding abilities of llms, in this paper, we propose a benchmark named graphinstruct, which comprehensively includes 21. Gtg can be installed with pip Empowering large language models with graph understanding and reasoning capability

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Zihan luo, xiran song, hong huang, jianxun lian,. Graph is a common data structure in the real world, and understanding graph data is a crucial. This is the benchmark proposed in our paper

Gtg can be installed with pip:.

本论文旨在评估和增强大型语言模型（LLMs）的图形理解能力，并提出了一个名为GraphInstruct的基准测试，包括21个经典图形推理任务，为提高LLMs的图形理解能力提. This benchmark is designed to evaluate and enhance the graph understanding abilities of large language models (llms) It includes 21 classical graph reasoning tasks, providing diverse. 为了评估和增强法学硕士的图理解能力，本文提出了一个名为GraphInstruct的基准测试，它全面包含21个经典图推理任务，提供多样化的图生成管道和详细的推理步骤。

本文是LLM系列文章，针对《GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability》的翻译。 评估和增强大型语言. Bilities of large language models (llms) has been an important research topic