Zero-shot learning (ZL) is crucial for tasks involving unseen categories, such as natural language processing, image classification, and cross-lingual transfer. Current applications often fail to accurately infer and handle new relations or entities involving unseen categories, severely limiting their scalability and practicality in open-domain scenarios. ZL learning faces the challenge of effectively transferring semantic information of unseen categories in multi-modal knowledge graph (MMKG) embedding representation learning. In this paper, we propose ZSLLM, a framework for zero-shot embedding learning of MMKGs using large language models (LLMs). We leverage textual modality information of unseen categories as prompts to fully utilize the reasoning capabilities of LLMs, enabling semantic information transfer across different modalities for unseen categories. Through model-based learning, the embedding representation of unseen categories in MMKG is enhanced. Extensive experiments conducted on multiple real-world datasets demonstrate the superiority of our approach compared to state-of-the-art methods.

本研究解决了当前零样本学习在多模态知识图谱嵌入中面临的有效信息转移和新类别关系推断的挑战。我们提出了一种名为ZSLLM的新框架，利用大语言模型的推理能力，通过文本模态信息来增强未见类别的嵌入表示。实验结果表明，ZSLLM在多个真实世界数据集上优于现有技术方法，展现了其良好的扩展性和实用性。

基于大语言模型的零样本学习方法用于多模态知识图谱嵌入