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Efficient Multi-Task Adaptation of Large Speech Models Using Hierarchical Recurrent Adapters


Core Concepts
A hierarchical recurrent adapter module is introduced that achieves better parameter efficiency in large-scale multi-task adaptation scenarios compared to previous adapter-based approaches and full model fine-tuning.
Abstract
The paper introduces Hierarchical Recurrent Adapters (HRA) for efficient adaptation of large pre-trained speech models to multiple downstream tasks. HRA consists of a single shared recurrent controller network and multiple task-specific adapter heads. This design reduces the per-task parameter overhead compared to previous adapter methods. Key highlights: HRA outperforms previous adapter-based approaches as well as full model fine-tuning in both single-task and multi-task adaptation settings on automatic speech recognition (ASR) tasks. The smallest HRA adapter with a linear head achieves 6.2% WER on the voice search test set using only 814K parameters, which is 8x more parameter efficient than the Residual Adapter baseline. In the multi-task setting on the Euphonia dataset, the HRA with an FFN head achieves the best WER, closing the gap to the full fine-tuning baseline. The HRA exhibits a sub-linear growth in the number of parameters as the number of tasks increases, demonstrating its scalability. Ablation studies confirm the importance of the recurrent controller design in the HRA.
Stats
The pre-trained Universal Speech Model (USM) has 2 billion parameters and was pre-trained on 12 million hours of multilingual data. The multi-domain training corpus contains anonymized English utterances from voice search, far-field, and long-form domains. The Euphonia corpus consists of over 1 million utterances from 128 speakers with various speech impairments.
Quotes
"Our Hierarchical Recurrent Adapter (HRA) outperforms the previous adapter-based approaches as well as full model fine-tuning baseline in both single and multi-task adaptation settings when evaluated on automatic speech recognition tasks." "The adapter can be placed in parallel or sequential to an entire block or the FFN layers within the block. It utilizes a hidden layer bottleneck to reduce the number of parameters and avoid over-fitting on a small downstream task data." "To reduce the per-task parameter overhead, we introduce a hierarchical adapter approach dubbed Hierarchical Recurrent Adapter (HRA). HRA is equipped with a recurrent controller network and a set of task-level adapter heads."

Deeper Inquiries

How can the HRA approach be extended to other modalities beyond speech, such as vision or language tasks

The HRA approach can be extended to other modalities beyond speech by adapting the hierarchical and recurrent structure to suit the specific requirements of vision or language tasks. For vision tasks, the shared controller network can be designed to process image features at different layers of a pre-trained vision model. Task-specific adapter heads can then be added to modify these features based on the target task. This adaptation can help in tasks like object detection, image classification, or segmentation, where fine-tuning specific parts of a pre-trained model is crucial. Similarly, for language tasks, the HRA architecture can be applied to pre-trained language models like BERT or GPT. The shared controller can interact with the layers of the language model, while task-specific adapter heads can adjust the representations for different NLP tasks such as sentiment analysis, question answering, or text generation. By reusing the controller and adapting only task-specific parameters, the HRA can efficiently adapt large language models to diverse downstream tasks.

What are the potential limitations of the HRA design, and how could it be further improved to handle more diverse or challenging adaptation scenarios

While the HRA design offers advantages in parameter efficiency and performance, there are potential limitations that need to be addressed for handling more diverse or challenging adaptation scenarios. One limitation is the scalability of the shared controller network as the number of tasks increases. To improve this, techniques like dynamic controller allocation or adaptive controller structures can be explored to efficiently manage a large number of tasks without compromising performance. Another limitation is the adaptability of the task-specific adapter heads to highly specialized tasks or domains. Enhancements such as incorporating domain-specific knowledge or meta-learning techniques can help the adapter heads better adapt to complex tasks with limited data. Additionally, exploring more advanced adapter architectures beyond linear projections or FFNs, such as attention-based adapters or graph neural networks, can further enhance the adaptability of the HRA in handling diverse adaptation scenarios.

Given the focus on parameter efficiency, how could the HRA be leveraged to enable efficient deployment of large pre-trained models on resource-constrained edge devices

To enable efficient deployment of large pre-trained models on resource-constrained edge devices, the HRA can play a crucial role in optimizing model adaptation and inference. By leveraging the parameter-efficient nature of the HRA, edge devices can adapt pre-trained models to specific tasks without the need for extensive retraining or fine-tuning. This can significantly reduce the computational and memory requirements on edge devices while maintaining task performance. One approach to leverage the HRA for edge deployment is to optimize the adapter heads for low-latency inference. By designing lightweight adapter architectures and optimizing the controller network for fast adaptation, the HRA can efficiently adapt models on the edge in real-time. Additionally, techniques like knowledge distillation or quantization can be applied to further compress the adapted models, making them more suitable for deployment on resource-constrained devices without sacrificing performance.
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