Analogical Reasoning with Multimodal Knowledge Graphs: Fine-Tuning Model Performance Based on LoRA
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Abstract
Multimodal knowledge graphs have recently been successfully applied to tasks such as those relating to information retrieval, question and answer, and recommender systems. In this study, we propose a dual-path fine-tuning mechanism technique with a low-rank adapter and an embedded cueing layer, aiming to improve the generalization and accuracy of the model in analogical reasoning tasks. The low-rank fine-tuning (LoRA) technique with rank-stable scaling factor is used to fine-tune the MKGformer model, and a cue-embedding layer is innovatively added to the input layer, which enables the model to better grasp the scale of the relationship between entities according to the dynamic cue vectors during the fine-tuning process and ensures that the model achieves the best results during training. The experimental results show that the R-MKG model improves several evaluation indexes by more than 20%, which is significantly better than the traditional DoRA and FA-LoRA methods. This research provides technical support for multimodal knowledge graph analogical reasoning. We hope that our work will bring benefits and inspire future research.
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