Sustainable NLP: Exploring Parameter Efficiency for Resource-Constrained Environments
Keywords:
Transfer Learning, Natural Language Processing (NLP), Adapter Modules, Parameter-Efficient, Fine-Tuning, Computational Efficieny, GLUE BenchmarkAbstract
Transfer learning is a critical component of the natural language processing (NLP) field, allowing the design of models that benefit from a pre-learned basis to perform well across all sorts of downstream tasks. Despite the success, the conventional fine-tuning process for full-scale models is associated with several difficulties. In particular, the fine-tuning operation requires huge computational resources and large memory. This characteristic is indeed not deployment-friendly in real applications, especially in resource-constrained environments. To overcome the difficulty, the paper presents a novel method based on parameter-efficient transfer learning, which leverages the adapter modules. Adapter modules are lightweight neural network components that are carefully inserted between the layers of a pre-trained model. The key advantage of this approach is the possibility of adapting the entire model to a task without actually modifying the model, hence reducing the number of fine-tuned model parameters. We present a maximal rigorous series of experiments on the GLUE benchmark - a well-known analysis suite of tasks - as well as additional classification tasks. The experiments demonstrate that our approach using adapter modules achieves competitive matching performance with the full model fine-tuning process. Most significantly, we find that adjusting the size of such adapter modules can reach a sweet spot of trading model performance against parameter efficiency, catering to a practical solution when deploying NLP models in various settings. We compare to other parameter-efficient methods such as just fine-tuning the top layers or just tuning the layer normalization parameters. This comparison underpins the superior quality of the adapter module method: keeping high performance while significantly reducing computational cost. The implications of our research are profound. In demonstrating the effectiveness of adapter modules, we pave the way for advanced and wider use of NLP models in accessible and adaptable ways. In practice, this is another step in the direction of democratizing the use of state-of-the-art NLP technologies, making them feasible for a wider range of applications and users. To make a concluding remark, our work exemplifies the potential of adapter modules in changing the landscape of NLP transfer learning. In this way, we ensure that large-scale models are efficiently fine-tuned, paving the way for their broad application in a large number of real-world scenarios. This is helpful not only for the more practical application of NLP models but also in furthering exploration into parameter-efficient learning methodologies.
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