Grasping the Essentials: Tailoring Large Language Models for Zero-Shot
  Relation Extraction
Sizhe Zhou Yu Meng Bowen Jin Jiawei Han
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.