AI RESEARCH
Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
arXiv CS.LG
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ArXi:2605.27913v1 Announce Type: new Node classification on graphs often requires labeled nodes, yet obtaining labels at graph scale is expensive. When node attributes contain semantic content, such as paper abstracts, web pages, or product descriptions, large language models (LLMs) can provide low-cost supervision by annotating a small subset of nodes. However, these LLM-generated labels are noisy, and existing label-free graph learning methods usually treat this noise as either global or class-conditional.