ABBA-Adapters: Efficient and Expressive Fine-Tuning of Foundation Models

• URL: http://arxiv.org/abs/2505.14238v3
• Date: Thu, 02 Oct 2025 16:35:40 GMT
• Title: ABBA-Adapters: Efficient and Expressive Fine-Tuning of Foundation Models
• Authors: Raghav Singhal, Kaustubh Ponkshe, Rohit Vartak, Praneeth Vepakomma,
• Abstract summary: Large Language Models have demonstrated strong performance across a wide range of tasks, but adapting them efficiently to new domains remains a key challenge.<n>We introduce ABBA, a new PEFT architecture that re parameterizes the update as a Hadamard product of two independently learnable low-rank matrices.<n>In contrast to prior work, ABBA fully decouples the update from the pre-trained weights, enabling both components to be optimized freely.
• Score: 10.17362679822278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models have demonstrated strong performance across a wide range of tasks, but adapting them efficiently to new domains remains a key challenge. Parameter-Efficient Fine-Tuning (PEFT) methods address this by introducing lightweight, trainable modules while keeping most pre-trained weights fixed. The prevailing approach, LoRA, models updates using a low-rank decomposition, but its expressivity is inherently constrained by the rank. Recent methods like HiRA aim to increase expressivity by incorporating a Hadamard product with the frozen weights, but still rely on the structure of the pre-trained model. We introduce ABBA, a new PEFT architecture that reparameterizes the update as a Hadamard product of two independently learnable low-rank matrices. In contrast to prior work, ABBA fully decouples the update from the pre-trained weights, enabling both components to be optimized freely. This leads to significantly higher expressivity under the same parameter budget, a property we validate through matrix reconstruction experiments. Empirically, ABBA achieves state-of-the-art results on arithmetic and commonsense reasoning benchmarks, consistently outperforming existing PEFT methods by a significant margin across multiple models. Our code is publicly available at: https://github.com/CERT-Lab/abba.

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