基于大语言模型的多模态知识图谱嵌入零样本学习方法
A Zero-shot Learning Method Based on Large Language Models for Multi-modal Knowledge Graph Embedding
AI总结：
针对多模态知识图谱嵌入中未见类别语义迁移难题，提出基于大语言模型的零样本学习框架ZSLLM，利用未见类别文本信息作为提示实现跨模态语义迁移，实验证明其优于现有方法。
摘要：
零样本学习（ZL）对于涉及未见类别的任务至关重要，例如自然语言处理、图像分类和跨语言迁移。当前应用往往无法准确推断和处理涉及未见类别的新关系或实体，严重限制了其在开放域场景中的可扩展性和实用性。ZL学习在多模态知识图谱（MMKG）嵌入表示学习中面临有效迁移未见类别语义信息的挑战。本文提出ZSLLM，一个使用大语言模型（LLMs）进行MMKG零样本嵌入学习的框架。我们利用未见类别的文本模态信息作为提示，充分利用LLMs的推理能力，实现未见类别在不同模态间的语义信息迁移。通过基于模型的学习，增强了MMKG中未见类别的嵌入表示。在多个真实世界数据集上的大量实验表明，与最先进方法相比，我们的方法具有优越性。
英文摘要：
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 orentities involving unseen categories, severely limiting their scalability and prac-ticality in open-domain scenarios. ZL learning faces the challenge of effectivelytransferring semantic information of unseen categories in multi-modal knowledgegraph (MMKG) embedding representation learning. In this paper, we proposeZSLLM, a framework for zero-shot embedding learning of MMKGs using largelanguage models (LLMs). We leverage textual modality information of unseencategories as prompts to fully utilize the reasoning capabilities of LLMs, enablingsemantic information transfer across different modalities for unseen categories.Through model-based learning, the embedding representation of unseen cate-gories in MMKG is enhanced. Extensive experiments conducted on multiplereal-world datasets demonstrate the superiority of our approach compared tostate-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
