Comparative Analysis of Efficient Adapter-Based Fine-Tuning of State-of-the-Art Transformer Models

Saad Mashkoor Siddiqui; Mohammad Ali Sheikh; Muhammad Aleem; Kajol R Singh

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arXiv · 2501.08271

Comparative Analysis of Efficient Adapter-Based Fine-Tuning of State-of-the-Art Transformer Models

Saad Mashkoor Siddiqui·Mohammad Ali Sheikh·Muhammad Aleem·Kajol R Singh

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Abstract

In this work, we investigate the efficacy of various adapter architectures on supervised binary classification tasks from the SuperGLUE benchmark as well as a supervised multi-class news category classification task from Kaggle. Specifically, we compare classification performance and time complexity of three transformer models, namely DistilBERT, ELECTRA, and BART, using conventional fine-tuning as well as nine state-of-the-art (SoTA) adapter architectures. Our analysis reveals performance differences across adapter architectures, highlighting their ability to achieve comparable or better performance relative to fine-tuning at a fraction of the training time. Similar results are observed on the new classification task, further supporting our findings and demonstrating adapters as efficient and flexible alternatives to fine-tuning. This study provides valuable insights and guidelines for selecting and implementing adapters in diverse natural language processing (NLP) applications.

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Saad Mashkoor Siddiqui, Mohammad Ali Sheikh, Muhammad Aleem, Kajol R Singh. 2025-01-14. Comparative Analysis of Efficient Adapter-Based Fine-Tuning of State-of-the-Art Transformer Models. https://arxiv.org/abs/2501.08271

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