BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language Models

TL;DR

BA-LoRA is a novel fine-tuning method for large language models that effectively reduces biases, noise propagation, and data imbalance issues inherent in existing low-rank adaptation techniques, enhancing robustness and fairness.

Contribution

This paper introduces BA-LoRA, a new regularization-based approach that systematically mitigates catastrophic inheritance in PEFT by addressing knowledge drift, representation collapse, and overfitting.

Findings

BA-LoRA outperforms existing LoRA variants in accuracy and stability.

It demonstrates superior robustness and bias mitigation on multiple NLP tasks.

The method effectively counters adverse effects of catastrophic inheritance.

Abstract

Parameter-efficient fine-tuning (PEFT) has become a de facto standard for adapting Large Language Models (LLMs). However, we identify a critical vulnerability within popular low-rank adaptation methods like LoRA: their tendency to exacerbate "Catastrophic Inheritance" - the unchecked propagation of biases, noise, and data imbalances from pre-training. This phenomenon can degrade model robustness and fairness, undermining the benefits of efficient adaptation. To address this, we introduce Bias-Alleviating Low-Rank Adaptation (BA-LoRA). Our approach is founded on a principled decomposition of Catastrophic Inheritance into three core challenges: Knowledge Drift, Representation Collapse, and Overfitting to Noise. BA-LoRA systematically mitigates these issues by incorporating a trio of targeted regularizers - consistency, diversity, and SVD - designed to preserve core knowledge, enforce representational richness, and promote robust, low-rank output representations. We conduct comprehensive evaluations on a suite of natural language understanding (NLU) and generation (NLG) tasks using diverse, prominent open-source language models (e.g., LLaMA-2-7B and DeBERTa-v3-base). Our results show that BA-LoRA not only outperforms state-of-the-art LoRA variants in terms of performance and stability, but also demonstrates quantitatively superior robustness and bias mitigation on targeted evaluations. This confirms its ability to counteract the adverse effects of Catastrophic Inheritance.