Relation extraction (RE) is an important task that aims to identify the
relationships between entities in texts. While large language models (LLMs)
have revealed remarkable in-context learning (ICL) capability for general zero
and few-shot learning, recent studies indicate that current LLMs still struggle
with zero and few-shot RE. Previous studies are mainly dedicated to design
prompt formats and select good examples for improving ICL-based RE. Although
both factors are vital for ICL, if one can fundamentally boost the ICL
capability of LLMs in RE, the zero and few-shot RE performance via ICL would be
significantly improved. To this end, we introduce \textsc{Micre} (\textbf{M}eta
\textbf{I}n-\textbf{C}ontext learning of LLMs for \textbf{R}elation
\textbf{E}xtraction), a new meta-training framework for zero and few-shot RE
where an LLM is tuned to do ICL on a diverse collection of RE datasets (i.e.,
learning to learn in context for RE). Through meta-training, the model becomes
more effectively to learn a new RE task in context by conditioning on a few
training examples with no parameter updates or task-specific templates at
inference time, enabling better zero and few-shot task generalization. We
experiment \textsc{Micre} on various LLMs with different model scales and 12
public RE datasets, and then evaluate it on unseen RE benchmarks under zero and
few-shot settings. \textsc{Micre} delivers comparable or superior performance
compared to a range of baselines including supervised fine-tuning and typical
in-context learning methods. We find that the gains are particular significant
for larger model scales, and using a diverse set of the meta-training RE
datasets is key to improvements. Empirically, we show that \textsc{Micre} can
transfer the relation semantic knowledge via relation label name during
inference on target RE datasets.

通过元训练框架（Micre）提高零样本和小样本关系抽取任务中大语言模型（LLMs）的上下文学习能力，实现更好的任务泛化。