arXiv:2605.27913v1 Announce Type: new Abstract: 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. We find that LLM annotation errors are not only class-dependent but also re
The proliferation of LLMs creates new avenues for data annotation, but understanding their limitations on graph data is critical for robust AI development.
This research highlights a significant limitation in using LLMs for data labeling on complex graph structures, directly impacting the cost and accuracy of machine learning models.
The understanding of LLM annotation errors is refined, moving from simple global/class-conditional noise to a more nuanced view of their context-dependent failures, necessitating new label-free learning methods.
- · AI researchers specializing in graph neural networks
- · Developers of new label-free learning algorithms
- · Companies with high-quality, human-annotated graph datasets
- · Platforms relying solely on LLM-generated labels for graph data
- · Applications where LLM-annotated data is used without robust error correction
Further research and development in robust label-free graph learning methods will accelerate.
New standards for evaluating LLM annotation quality on structured data will emerge, leading to more reliable AI systems.
The development of hybrid annotation strategies combining human expertise with LLM capabilities will become prevalent, optimizing cost and quality.
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Read at arXiv cs.LG