Low-rank Finetuning For Llms: A Fairness Perspective

Das Saswat, Romanelli Marco, Tran Cuong, Reza Zarreen, Kailkhura Bhavya, Fioretto Ferdinando. Arxiv 2024

[Paper]    
Bias Mitigation Efficiency And Optimization Ethics And Bias Fairness Fine Tuning Pretraining Methods Training Techniques

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.

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