{"url":"/dataset/graphinstruct","name":"GraphInstruct","full_name":null,"description_markdown":"The GraphInstruct dataset is part of a benchmark proposed in the paper titled \"GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability.\" 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 graph generation pipelines and detailed reasoning steps².\r\n\r\nThe dataset is used to construct models like GraphLM and GraphLM+, which are trained through efficient instruction-tuning and a step mask training strategy to show prominent graph understanding capability. These models have demonstrated superiority over other LLMs in understanding and reasoning with graph data².\r\n\r\n(1) GraphInstruct: Empowering Large Language Models with Graph .... https://arxiv.org/abs/2403.04483.\r\n(2) CGCL-codes/GraphInstruct - GitHub. https://github.com/CGCL-codes/GraphInstruct.\r\n(3) GraphWiz/GraphInstruct · Datasets at Hugging Face. https://huggingface.co/datasets/GraphWiz/GraphInstruct/viewer.\r\n(4) GraphWiz: An Instruction-Following Language Model for Graph Problems. https://arxiv.org/abs/2402.16029.\r\n(5) undefined. https://doi.org/10.48550/arXiv.2403.04483.","description_withheld":null,"homepage":"https://github.com/CGCL-codes/GraphInstruct","introduced_date":"2024-03-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/graphinstruct-empowering-large-language","title":"GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability","first_author":"Zihan Luo","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["GraphInstruct"],"data_loaders":[],"num_papers_in_archive":10,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}