Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction

• URL: http://arxiv.org/abs/2402.11142v1
• Date: Sat, 17 Feb 2024 00:20:06 GMT
• Title: Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction
• Authors: Sizhe Zhou, Yu Meng, Bowen Jin, Jiawei Han
• Abstract summary: Relation extraction (RE) aims to identify semantic relationships between entities mentioned in texts. Few-shot learning settings may offer incomplete and biased supervision for understanding target relation semantics. We study the definition only zero-shot RE setting where only relation definitions expressed in natural language are used to train a RE model.
• Score: 36.627683488532234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Relation extraction (RE), a crucial task in NLP, aims to identify semantic relationships between entities mentioned in texts. Despite significant advancements in this field, existing models typically rely on extensive annotated data for training, which can be both costly and time-consuming to acquire. Moreover, these models often struggle to adapt to new or unseen relationships. In contrast, few-shot learning settings, which aim to reduce annotation requirements, may offer incomplete and biased supervision for understanding target relation semantics, leading to degraded and unstable performance. To provide the model with accurate and explicit descriptions of the relations types and meanwhile minimize the annotation requirements, we study the definition only zero-shot RE setting where only relation definitions expressed in natural language are used to train a RE model. Motivated by the strong synthetic data generation power of LLMs, we propose a framework REPaL which consists of three stages: (1) We utilize LLMs to generate initial seed instances based on relation definitions and an unlabeled corpora. (2) We fine-tune a bidirectional Small Language Model (SLM) using these initial seeds to learn the relations for the target domain. (3) We enhance pattern coverage and mitigate bias resulting from the limited number of initial seeds by incorporating feedback acquired from SLM's predictions on unlabeled corpora. To accomplish this, we leverage the multi-turn conversation ability of LLMs to generate new instances in follow-up dialogues. Experiments on two datasets show REPaL achieves better zero-shot performance with large margins over baseline methods.

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