Low-rank finetuning for LLMs: A fairness perspective
<|reference_start|>Low-rank finetuning for LLMs: A fairness perspective: Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models (LLMs) due to their reduced computational and memory requirements. This paper investigates the effectiveness of these methods in capturing the shift of fine-tuning datasets from the initial pre-trained data distribution. Our findings reveal that there are cases in which low-rank fine-tuning falls short in learning such shifts. This, in turn, produces non-negligible side effects, especially when fine-tuning is adopted for toxicity mitigation in pre-trained models, or in scenarios where it is important to provide fair models. Through comprehensive empirical evidence on several models, datasets, and tasks, we show that low-rank fine-tuning inadvertently preserves undesirable biases and toxic behaviors. We also show that this extends to sequential decision-making tasks, emphasizing the need for careful evaluation to promote responsible LLMs development.<|reference_end|>
@article{das2024low-rank,
    title={Low-rank finetuning for LLMs: A fairness perspective},
    author={Saswat Das, Marco Romanelli, Cuong Tran, Zarreen Reza, Bhavya
  Kailkhura, Ferdinando Fioretto},
    journal={arXiv preprint arXiv:2405.18572},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18572},
    primaryClass={cs.LG cs.AI cs.CL}
}