GPT-RE: In-context Learning for Relation Extraction using Large Language Models
This work addresses the gap in relation extraction for NLP applications, offering an incremental improvement over existing methods.
The paper tackles the problem of large language models (LLMs) underperforming in relation extraction (RE) compared to fully-supervised baselines, and proposes GPT-RE, which improves performance by incorporating entity representations and reasoning logic in demonstrations, achieving state-of-the-art results on Semeval and SciERC datasets and competitive results on TACRED and ACE05.
In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e.g., GPT-3), they still lag significantly behind fully-supervised baselines (e.g., fine-tuned BERT) in relation extraction (RE). This is due to the two major shortcomings of LLMs in RE: (1) low relevance regarding entity and relation in retrieved demonstrations for in-context learning; and (2) the strong inclination to wrongly classify NULL examples into other pre-defined labels. In this paper, we propose GPT-RE to bridge the gap between LLMs and fully-supervised baselines. GPT-RE successfully addresses the aforementioned issues by (1) incorporating task-specific entity representations in demonstration retrieval; and (2) enriching the demonstrations with gold label-induced reasoning logic. We evaluate GPT-RE on four widely-used RE datasets, and observe that GPT-RE achieves improvements over not only existing GPT-3 baselines, but also fully-supervised baselines. Specifically, GPT-RE achieves SOTA performances on the Semeval and SciERC datasets, and competitive performances on the TACRED and ACE05 datasets.