Relation extraction represents a fundamental component in the process of creating knowledge graphs, among other applications. Large language models (LLMs) have been adopted as a promising tool for relation extraction, both in supervised and in-context learning settings. However, in this work we show that their performance still lags behind much smaller architectures when the linguistic graph underlying a text has great complexity. To demonstrate this, we evaluate four LLMs against a graph-based parser on six relation extraction datasets with sentence graphs of varying sizes and complexities. Our results show that the graph-based parser increasingly outperforms the LLMs, as the number of relations in the input documents increases. This makes the much lighter graph-based parser a superior choice in the presence of complex linguistic graphs.
@article{arxiv.2604.08752,
title = {LLMs Underperform Graph-Based Parsers on Supervised Relation Extraction for Complex Graphs},
author = {Paolo Gajo and Domenic Rosati and Hassan Sajjad and Alberto Barrón-Cedeño},
journal= {arXiv preprint arXiv:2604.08752},
year = {2026}
}