Leveraging Hierarchical Taxonomies in Prompt-based Continual Learning

2024-10-06

arXiv.org

AI Summary

To mitigate catastrophic forgetting in prompt-based continual learning, this paper proposes a cognitively inspired confusion-aware method. First, it constructs a dynamic hierarchical label tree to model inter-class semantic relationships. Second, it leverages optimal transport to analyze the implicit knowledge structure of pretrained models and identify fine-grained discriminative regions prone to confusion. Finally, within a prompt-tuning framework, it introduces a novel class-relation-aware regularization loss that explicitly constrains prompt vector updates in confusion-sensitive regions. This work is the first to jointly integrate a dynamic hierarchical taxonomy with optimal-transport-driven knowledge relationship mining into prompt tuning. Extensive experiments on multiple continual learning benchmarks demonstrate that the proposed method significantly outperforms state-of-the-art approaches, effectively alleviating forgetting and improving overall accuracy across both old and new tasks.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Search and Optimization: Learning to Search
• Natural Language Processing: Prompt Engineering / Prompting

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Humans perceive the world as a series of sequential events, which can be hierarchically organized with different levels of abstraction based on conceptual knowledge. Drawing inspiration from human learning behaviors, this work proposes a novel approach to mitigate catastrophic forgetting in Prompt-based Continual Learning models by exploiting the relationships between continuously emerging class data. We find that applying human habits of organizing and connecting information can serve as an efficient strategy when training deep learning models. Specifically, by building a hierarchical tree structure based on the expanding set of labels, we gain fresh insights into the data, identifying groups of similar classes could easily cause confusion. Additionally, we delve deeper into the hidden connections between classes by exploring the original pretrained model's behavior through an optimal transport-based approach. From these insights, we propose a novel regularization loss function that encourages models to focus more on challenging knowledge areas, thereby enhancing overall performance. Experimentally, our method demonstrated significant superiority over the most robust state-of-the-art models on various benchmarks.

Problem

• Mitigates catastrophic forgetting in continual learning models.
• Exploits hierarchical relationships among emerging class data.
• Enhances model performance through novel regularization techniques.

Innovation

• Hierarchical tree structure for label organization
• Optimal transport-based class connection exploration
• Novel regularization loss for challenging knowledge