Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors

• URL: http://arxiv.org/abs/2305.11159v1
• Date: Thu, 18 May 2023 17:48:03 GMT
• Title: Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors
• Authors: Kai Zhang, Bernal Jim\'enez Guti\'errez, Yu Su
• Abstract summary: Fine-tuning large language models (LLMs) on large-scale instruction-following datasets substantially improves their performance on a wide range of NLP tasks. However, even advanced instruction-tuned LLMs still fail to outperform small LMs on relation extraction (RE) We propose QA4RE, a framework that aligns RE with question answering (QA), a predominant task in instruction-tuning datasets.
• Score: 11.28397947587596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work has shown that fine-tuning large language models (LLMs) on large-scale instruction-following datasets substantially improves their performance on a wide range of NLP tasks, especially in the zero-shot setting. However, even advanced instruction-tuned LLMs still fail to outperform small LMs on relation extraction (RE), a fundamental information extraction task. We hypothesize that instruction-tuning has been unable to elicit strong RE capabilities in LLMs due to RE's low incidence in instruction-tuning datasets, making up less than 1% of all tasks (Wang et al., 2022). To address this limitation, we propose QA4RE, a framework that aligns RE with question answering (QA), a predominant task in instruction-tuning datasets. Comprehensive zero-shot RE experiments over four datasets with two series of instruction-tuned LLMs (six LLMs in total) demonstrate that our QA4RE framework consistently improves LLM performance, strongly verifying our hypothesis and enabling LLMs to outperform strong zero-shot baselines by a large margin. Additionally, we provide thorough experiments and discussions to show the robustness, few-shot effectiveness, and strong transferability of our QA4RE framework. This work illustrates a promising way of adapting LLMs to challenging and underrepresented tasks by aligning these tasks with more common instruction-tuning tasks like QA.

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