LLMs Underperform Graph-Based Parsers on Supervised Relation Extraction for Complex Graphs
Summary: arXiv:2604.08752v1
Announce Type: cross
Abstract: 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.
Introduction
Relation extraction is a pivotal task in the field of natural language processing (NLP) and plays a crucial role in the construction of knowledge graphs. Knowledge graphs serve as structured representations of information, facilitating better understanding and retrieval of data. Recently, there has been an increasing interest in utilizing Large Language Models (LLMs) for relation extraction due to their ability to understand and generate human-like text.
Key Findings
This study evaluates the performance of four prominent LLMs against a graph-based parser across six relation extraction datasets. The evaluation focuses on input sentences that are represented as linguistic graphs, which vary in both size and complexity. The findings indicate that:
- The graph-based parser consistently outperforms LLMs as the complexity of the input increases.
- As the number of relations within the input documents rises, the performance gap between the LLMs and the graph-based parser widens.
- Despite their larger architectures, LLMs struggle to effectively manage complex linguistic structures compared to smaller, specialized models.
Methodology
The methodology involved comprehensive testing of LLMs and the graph-based parser using six diverse datasets. Each dataset was meticulously designed to include a variety of sentence graphs that presented different levels of complexity. The performance metrics used for evaluation included precision, recall, and F1 score, allowing for a nuanced understanding of how each model performed under varying conditions.
Implications
The results of this study have significant implications for the field of NLP and the development of knowledge graphs. While LLMs have garnered attention for their capabilities, this research highlights the importance of considering alternative approaches, particularly in scenarios characterized by complex linguistic structures.
Researchers and practitioners may benefit from utilizing graph-based parsers, especially in applications where relation extraction from complex graphs is necessary. This could lead to more efficient and accurate knowledge graph creation, ultimately enhancing data retrieval and understanding.
Conclusion
In light of the findings, it is clear that while LLMs represent a significant advancement in NLP, they may not be the optimal choice for all relation extraction tasks, particularly those involving complex linguistic graphs. The graph-based parser emerges as a robust alternative, capable of outperforming LLMs in specific contexts. Future research should further explore the integration of various models to leverage the strengths of both LLMs and graph-based approaches, potentially leading to enhanced performance in relation extraction tasks.