# A Zero-shot Learning Method Based on Large Language Models for Multi-modal Knowledge Graph Embedding

arXiv: https://arxiv.org/abs/2503.07202 · HTML: https://mlsanity.com/paper/2503.07202/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs](https://mlsanity.com/paper/1803.08035/) (2018)
- [Zero-Shot Learning by Convex Combination of Semantic Embeddings](https://mlsanity.com/paper/1312.5650/) (2013)
- [Generalized Zero-Shot Learning via Synthesized Examples](https://mlsanity.com/paper/1712.03878/) (2017)
- [Synthesized Classifiers for Zero-Shot Learning](https://mlsanity.com/paper/1603.00550/) (2016)
- [Rethinking Knowledge Graph Propagation for Zero-Shot Learning](https://mlsanity.com/paper/1805.11724/) (2018)
- [Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning](https://mlsanity.com/paper/1605.08151/) (2016)
- [Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training](https://mlsanity.com/paper/2305.07633/) (2023)
- [Zero-Shot Kernel Learning](https://mlsanity.com/paper/1802.01279/) (2018)

## Abstract (arXiv)

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.