How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?

• URL: http://arxiv.org/abs/2305.01555v4
• Date: Fri, 9 Jun 2023 15:59:18 GMT
• Title: How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?
• Authors: Xin Xu, Yuqi Zhu, Xiaohan Wang, Ningyu Zhang
• Abstract summary: In this paper, we investigate principal methodologies, in-context learning and data generation, for few-shot relation extraction via GPT-3.5. We observe that in-context learning can achieve performance on par with previous prompt learning approaches, and data generation with the large language model can boost previous solutions to obtain new state-of-the-art few-shot results.
• Score: 28.413620806193165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scaling language models have revolutionized widespread NLP tasks, yet little comprehensively explored few-shot relation extraction with large language models. In this paper, we investigate principal methodologies, in-context learning and data generation, for few-shot relation extraction via GPT-3.5 through exhaustive experiments. To enhance few-shot performance, we further propose task-related instructions and schema-constrained data generation. We observe that in-context learning can achieve performance on par with previous prompt learning approaches, and data generation with the large language model can boost previous solutions to obtain new state-of-the-art few-shot results on four widely-studied relation extraction datasets. We hope our work can inspire future research for the capabilities of large language models in few-shot relation extraction. Code is available in https://github.com/zjunlp/DeepKE/tree/main/example/llm.

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