Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks (2106.04489v1)

Abstract: State-of-the-art parameter-efficient fine-tuning methods rely on introducing adapter modules between the layers of a pretrained LLM. However, such modules are trained separately for each task and thus do not enable sharing information across tasks. In this paper, we show that we can learn adapter parameters for all layers and tasks by generating them using shared hypernetworks, which condition on task, adapter position, and layer id in a transformer model. This parameter-efficient multi-task learning framework allows us to achieve the best of both worlds by sharing knowledge across tasks via hypernetworks while enabling the model to adapt to each individual task through task-specific adapters. Experiments on the well-known GLUE benchmark show improved performance in multi-task learning while adding only 0.29% parameters per task. We additionally demonstrate substantial performance improvements in few-shot domain generalization across a variety of tasks. Our code is publicly available in https://github.com/rabeehk/hyperformer.

Analyzing Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks

The paper introduces a novel approach, termed HyperFormer, which addresses the challenge of parameter-efficient multi-task fine-tuning of transformer models, specifically focusing on enhancing information sharing across tasks through shared hypernetworks. These hypernetworks condition on task, adapter position, and layer ID within transformers like T5, enabling the generation of adapter parameters for all tasks and layers. This approach promises an optimized balance between shared knowledge across tasks and the unique adaptability requirements of individual tasks, presenting a significant advancement in the field of transfer learning and NLP model efficiency.

Theoretical and Practical Contributions

1. Integration of Shared Hypernetworks:

The paper's methodology involves leveraging shared hypernetworks to adjust the parameters of task-specific adapter layers across a transformer model. The hypernetworks aim to maintain the benefits of task-specific adapters, as proposed by prior works, while overcoming the limitation of separate learning for each task-specific adapter, which can impede positive transfer and increase parameter inefficiency. This differentiation is particularly notable, as it situates HyperFormer uniquely in the field by minimizing task interference and enabling adaptive learning at a fine-grained level across different domains.

2. Reduced Parameter Overhead:

The authors demonstrate that their approach results in only a 0.29% increase in parameters per task, an impressive feat compared to traditional transformer fine-tuning which often involves adjusting a far higher proportion of the model's parameters. This opens pathways for deploying models in resource-constrained environments where computational and memory efficiencies are paramount.

3. Empirical Validation:

The paper provides empirical validations using the GLUE benchmark, a robust suite of tasks for NLP systems. HyperFormer not only achieves competitive performance across these tasks but also showcases substantial improvements in few-shot domain generalization, thereby underlining the method's robustness and versatility.

4. Low-Resource Settings and Generalization:

Further analyses indicate that HyperFormer holds particular promise for low-resource settings, demonstrating superior performance when data is scarce. This is crucial for applications in less commonly studied languages or specialized domains where large datasets are unavailable.

Implications and Future Directions

The implications of this research are multifaceted. Theoretically, it challenges existing paradigms of multi-task learning by presenting a method that effectively combines task-specificity with cross-task knowledge sharing. Practically, it supports the deployment of sophisticated LLMs in resource-limited settings, thus broadening the potential impact of AI technologies in various socio-economic domains.

Given the findings, future developments might explore refining hypernetwork architectures to further enhance efficiency or integrating this approach with other promising techniques like meta-learning for even better adaptability. Furthermore, the application of HyperFormer to other types of sequence models beyond transformers could expand its utility across different areas of machine learning.

Conclusion

The paper on HyperFormer effectively contributes to the ongoing discourse on optimizing transformer models for multi-task learning. Its careful balancing of parameter efficiency with task adaptability provides a significant step forward in the design of AI systems that are both powerful and resource-conscious. The release of the associated code also emphasizes the authors’ commitment to fostering continued research and development in this promising area of AI technology.