On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation (2106.03164v1)

Abstract: Adapter-based tuning has recently arisen as an alternative to fine-tuning. It works by adding light-weight adapter modules to a pretrained LLM (PrLM) and only updating the parameters of adapter modules when learning on a downstream task. As such, it adds only a few trainable parameters per new task, allowing a high degree of parameter sharing. Prior studies have shown that adapter-based tuning often achieves comparable results to fine-tuning. However, existing work only focuses on the parameter-efficient aspect of adapter-based tuning while lacking further investigation on its effectiveness. In this paper, we study the latter. We first show that adapter-based tuning better mitigates forgetting issues than fine-tuning since it yields representations with less deviation from those generated by the initial PrLM. We then empirically compare the two tuning methods on several downstream NLP tasks and settings. We demonstrate that 1) adapter-based tuning outperforms fine-tuning on low-resource and cross-lingual tasks; 2) it is more robust to overfitting and less sensitive to changes in learning rates.

Adapter-based Tuning: Assessing Its Effectiveness in Pretrained LLM Adaptation

The paper under consideration presents a detailed examination of adapter-based tuning, a method of adapting pretrained LLMs (PrLMs) in contrast to the traditionally adopted fine-tuning approach. This method incorporates lightweight adapter modules within transformer layers and updates only these adapters, leaving the PrLM's original weights untouched. The core advantage lies in its parameter efficiency, allowing multiple task adaptations without substantial parameter growth.

Key Findings

1. Forgetting Mitigation: One significant contribution of this paper is the evidence supporting the claim that adapter-based tuning alleviates the issue of catastrophic forgetting, a challenge often faced in model adaptation. By maintaining the original model parameters intact and only allowing adaptations through internal layers, the representations generated post-adaptation demonstrate reduced deviation from the baseline, indicatively preserving learned information from the pretraining stage.
2. Empirical Comparison:
   • Monolingual Setting: Adapter-based tuning shows superior performance, particularly in low-resource settings. When tasked with domain-specific challenges, its benefits are amplified. This advantage diminishes as the volume of training data increases.
   • Cross-Lingual Tasks: For zero-shot cross-lingual tasks, adapter-based tuning outperformed fine-tuning, demonstrating robustness even with varying training data sizes. This indicates its effectiveness in leveraging pretraining knowledge across languages, crucially important given the diverse linguistic structures.
3. Training Stability: The paper identifies that adapter-based tuning is less sensitive to variations in learning rates compared to fine-tuning. This stability is reflected in smoother loss landscapes and higher mean performance consistency over training epochs, both haLLMarks of robust model adaptation.

Implications and Future Directions

Practical Implications:

The findings of this research suggest optimal scenarios for deploying adapter-based tuning, mainly when resource allocation is constrained or when task-specific domains diverge significantly from large-scale pretraining corpuses. Furthermore, the paper reveals its capacity for handling multilingual scenarios with enhanced effectiveness, contributing to its suitability in global applications where language diversity is a concern.

Theoretical Implications:

A key theoretical insight is the role of representation resilience within adapted neural architectures. The paper proposes that adapter modules, through their structural design using skip connections, inherit stability from pretraining, providing a promising avenue for studying how neural networks retain and propagate learned representations.

Future Developments:

Future research should aim to explore deeper integration of adapter-based methods across different PrLM architectures, examining its scalability and performance across broader NLP tasks. Additionally, investigating optimization strategies tailored to adapter configurations could refine its efficacy further, potentially harmonizing the trade-off between model capacity and training efficiency.

Conclusion

This paper delineates the advantages and comparative superiority of adapter-based tuning for pretrained LLM adaptations across various contexts. Its emphasis on stability and effectiveness in low-resource and cross-lingual settings positions it as a viable alternative to traditional fine-tuning, meriting further exploration in both research and applied NLP domains. As AI continues to evolve, understanding and improving how we adapt models efficiently will be paramount, and this research contributes to that trajectory substantially.