Lamer-SSL: Layer-aware Mixture of LoRA Experts for Continual Multilingual Expansion of Self-supervised Models without Forgetting
arXiv:2602.12746v1 Announce Type: new Abstract: Despite their impressive performance, self-supervised speech models often struggle to generalize to new languages and tend to forget previously acquired knowledge during continual training. To address this, we propose Lamer-SSL, a parameter-efficient framework that integrates a Layer-Aware MixturE of LoRA Experts (Lamer) module with a replay strategy. The Lamer module enables flexible balancing between shared and language-specific representations, while layer-aware expert allocation assigns more experts to deeper layers where semantic information is richer. Meanwhile, the replay strategy retains prior knowledge using minimal data, mitigating forgetting during continual training. Experiments on automatic speech recognition (ASR) and language identification (LID) demonstrate that Lamer-SSL extends self-supervised models to new languages effectively while maintaining strong performance on previously learned languages with only 2.14% paramet
arXiv:2602.12746v1 Announce Type: new Abstract: Despite their impressive performance, self-supervised speech models often struggle to generalize to new languages and tend to forget previously acquired knowledge during continual training. To address this, we propose Lamer-SSL, a parameter-efficient framework that integrates a Layer-Aware MixturE of LoRA Experts (Lamer) module with a replay strategy. The Lamer module enables flexible balancing between shared and language-specific representations, while layer-aware expert allocation assigns more experts to deeper layers where semantic information is richer. Meanwhile, the replay strategy retains prior knowledge using minimal data, mitigating forgetting during continual training. Experiments on automatic speech recognition (ASR) and language identification (LID) demonstrate that Lamer-SSL extends self-supervised models to new languages effectively while maintaining strong performance on previously learned languages with only 2.14% parameters being trainable.
Executive Summary
The article 'Lamer-SSL: Layer-aware Mixture of LoRA Experts for Continual Multilingual Expansion of Self-supervised Models without Forgetting' introduces a novel framework designed to enhance the adaptability and retention capabilities of self-supervised speech models. The proposed Lamer-SSL framework integrates a Layer-Aware Mixture of LoRA Experts (Lamer) module with a replay strategy, enabling efficient multilingual expansion while mitigating the issue of catastrophic forgetting. The Lamer module allows for flexible balancing between shared and language-specific representations, with a layer-aware expert allocation that prioritizes deeper layers where semantic information is more abundant. The replay strategy ensures the retention of prior knowledge using minimal data. Experimental results on automatic speech recognition (ASR) and language identification (LID) demonstrate the effectiveness of Lamer-SSL in extending models to new languages while maintaining performance on previously learned languages, with only 2.14% of parameters being trainable.
Key Points
- ▸ Introduction of Lamer-SSL framework for continual multilingual expansion of self-supervised models.
- ▸ Integration of Layer-Aware Mixture of LoRA Experts (Lamer) module and replay strategy.
- ▸ Layer-aware expert allocation prioritizes deeper layers for richer semantic information.
- ▸ Replay strategy retains prior knowledge using minimal data.
- ▸ Experiments show effective extension to new languages with minimal trainable parameters.
Merits
Parameter Efficiency
The framework achieves significant parameter efficiency, with only 2.14% of parameters being trainable, making it highly scalable and resource-efficient.
Mitigation of Catastrophic Forgetting
The replay strategy effectively retains prior knowledge, addressing the critical issue of catastrophic forgetting in continual learning.
Flexible Representation Balancing
The Lamer module allows for flexible balancing between shared and language-specific representations, enhancing the model's adaptability to new languages.
Demerits
Complexity of Implementation
The integration of multiple components, including the Lamer module and replay strategy, may increase the complexity of implementation and deployment.
Limited Experimental Scope
The experiments are focused on ASR and LID tasks, which may limit the generalizability of the findings to other speech processing tasks.
Data Requirements
The replay strategy, while minimal, still requires some data storage and retrieval mechanisms, which could be a limitation in resource-constrained environments.
Expert Commentary
The Lamer-SSL framework represents a significant advancement in the field of continual learning for self-supervised speech models. The integration of the Lamer module and replay strategy addresses two critical challenges: the need for parameter efficiency and the mitigation of catastrophic forgetting. The layer-aware expert allocation is particularly innovative, as it leverages the hierarchical nature of speech representations, assigning more experts to deeper layers where semantic information is richer. This approach not only enhances the model's adaptability to new languages but also ensures that previously acquired knowledge is retained effectively. The experimental results, demonstrating the framework's effectiveness in ASR and LID tasks, provide strong empirical support for its potential applications. However, the complexity of implementation and the limited scope of experiments are areas that warrant further investigation. Future research could explore the generalizability of the Lamer-SSL framework to other speech processing tasks and its applicability in resource-constrained environments. Overall, this work contributes valuable insights to the ongoing efforts to develop more adaptable, efficient, and scalable AI models.
Recommendations
- ✓ Further exploration of the Lamer-SSL framework's applicability to other speech processing tasks beyond ASR and LID.
- ✓ Investigation into simplifying the implementation process to make the framework more accessible for practical deployment.
- ✓ Development of strategies to optimize data storage and retrieval mechanisms for the replay strategy, particularly in resource-constrained environments.