LLMs Underperform Graph-Based Parsers on Supervised Relation Extraction for Complex Graphs

A new study comparing large language models against graph-based parsers for relation extraction demonstrates that smaller, specialized architectures significantly outperform LLMs when processing complex linguistic graphs with multiple relations. This finding challenges the prevailing assumption that larger language models are universally superior for natural language processing tasks.

The research presented in this arXiv paper addresses a critical assumption in modern AI development: that larger, more general-purpose language models automatically excel at specialized NLP tasks. The study evaluates four different LLMs against a lightweight graph-based parser across six relation extraction datasets, measuring performance as linguistic complexity increases. The results reveal a counterintuitive pattern where the graph-based parser's performance advantage grows proportionally with document complexity and the number of relations present.

This finding emerges within a broader context of LLM adoption across industry and research. Over the past two years, enterprises have invested heavily in deploying LLMs for knowledge graph construction and relation extraction, assuming their semantic understanding capabilities would translate to superior performance. The research challenges this narrative by demonstrating that specialized architectures remain effective—sometimes more effective—for constrained problems. Graph-based parsers leverage explicit structural information about linguistic dependencies, while LLMs rely on implicit learned representations, suggesting that for tasks with well-defined structural patterns, explicit approaches retain advantages.

For developers and organizations building knowledge graph pipelines, this research suggests strategic deployment choices. While LLMs offer flexibility for varied tasks and in-context learning capabilities, resource-constrained environments with complex linguistic inputs may achieve better results and efficiency gains using graph-based approaches. The implication extends beyond performance metrics to computational costs, where lightweight parsers consume substantially fewer resources than LLM inference.

Looking forward, this work suggests the AI development field may benefit from increased pragmatism regarding tool selection. Rather than pursuing monolithic LLM solutions, hybrid approaches combining specialized parsers for structured tasks with LLMs for context-dependent reasoning could optimize both performance and efficiency across diverse applications.

• →Graph-based parsers outperform four tested LLMs on relation extraction tasks with complex linguistic structures
• →LLM performance degradation increases as the number of relations and linguistic graph complexity rises
• →Lightweight specialized architectures offer superior efficiency and resource consumption compared to LLM inference
• →Task-specific solutions remain viable alternatives to general-purpose LLMs for well-defined NLP problems
• →Hybrid approaches combining multiple architectures may optimize performance across diverse relation extraction scenarios