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

Raghav Singhal, Kaustubh Ponkshe, Rohit Vartak, Praneeth Vepakomma

Keywords: LoRA, Low-rank adaptation, PEFT, Parameter-Efficient Fine-Tuning

TL;DR: We introduce ABBA, a PEFT method that enhances expressivity by decoupling low-rank updates from pre-trained weights via a Hadamard product, consistently improving over SOTA methods.

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.

Primary Area: transfer learning, meta learning, and lifelong learning

Submission Number: 3156