Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors

• URL: http://arxiv.org/abs/2404.17807v1
• Date: Sat, 27 Apr 2024 07:06:39 GMT
• Title: Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors
• Authors: Guozheng Li, Peng Wang, Jiajun Liu, Yikai Guo, Ke Ji, Ziyu Shang, Zijie Xu,
• Abstract summary: textscMicre (textbfMeta textbfIn-textbfContext learning of LLMs for textbfRelation textbfExtraction) is a new meta-training framework for zero and few-shot Relation extraction. We show that textscMicre can transfer the relation semantic knowledge via relation label name during inference on target RE datasets.
• Score: 9.881102419679673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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