Low-rank finetuning for LLMs: A fairness perspective

Abstract: Low-rank approximation techniques have become the de facto standard for fine-tuning 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.

• The paper demonstrates that low-rank fine-tuning techniques like LoRA fail to fully mitigate biases and toxic behaviors in large language models.
• The authors conduct comprehensive experiments across models and datasets to compare the fairness outcomes of low-rank and full-scale fine-tuning.
• The study uses statistical divergence metrics to highlight the limitations of LoRA in realigning model distributions for responsible and fair AI outputs.

An Analysis of Low-Rank Finetuning for Fairness in LLMs

"Low-rank finetuning for LLMs: A fairness perspective" addresses an important challenge in the deployment and fine-tuning of LLMs: the need to mitigate inherent biases and toxic behaviors within computational and memory constraints. The paper scrutinizes the efficacy of Low-Rank Adaptation (LoRA) techniques in aligning fine-tuned models with desired fairness objectives.

Summary of the Paper

The paper explores the prevalent use of low-rank approximation methods, particularly LoRA, as an efficient alternative to full-scale fine-tuning of LLMs. The authors examine the ability of these methods to encapsulate shifts in data distribution resulting from fine-tuning datasets. Their empirical findings suggest that LoRA, while computationally efficient, may not adequately capture significant distributional shifts necessary for mitigating biases and toxicity. This limitation poses challenges to the development of fair and responsible LLMs when employing low-rank fine-tuning techniques.

Key Contributions

1. Effectiveness of LoRA in Bias and Toxicity Mitigation: The study identifies that low-rank fine-tuning methods often fall short in realigning the output distribution of pre-trained models to mitigate toxic and biased behaviors. This shortfall is more pronounced at lower ranks, which are typically used in practice for their computational benefits.
2. Empirical Analysis Across Models and Datasets: Through comprehensive experiments, the authors demonstrate the persistence of undesirable behaviors in LoRA fine-tuned models, compared to their fully fine-tuned counterparts. This observation is supported by an analysis of model predictions at various transformer layers, revealing that low-rank methods retain much of the original model's toxic tendencies.
3. Quantitative Metrics and Qualitative Insights: The research employs robust quantitative metrics to evaluate harmful biases and accuracy disparities between majority and minority groups in downstream tasks. This evaluation emphasizes the susceptibility of lower-rank LoRA fine-tuned models to exacerbate unfair decision-making, particularly in sequential classification tasks.
4. Statistical Divergence Analysis: The study provides statistical evidence linking the effectiveness of fine-tuning methods to their ability to diverge from the original model's distribution over the token space. The findings indicate that LoRA models, especially at lower ranks, exhibit lower KL-divergence and therefore retain more of the original model's harmful characteristics.

Implications and Future Directions

The implications of these findings are manifold and significant. Practically, the research underscores a potential risk in adopting low-rank fine-tuning techniques without thorough evaluation of their fairness outcomes. Theoretically, it establishes a new framework for analyzing and understanding the limitations and capabilities of parameter-efficient fine-tuning methods like LoRA.

Looking forward, future developments in AI could focus on:

• Improved Fine-Tuning Techniques: Innovations could involve hybrid approaches that balance the efficiency of low-rank methods with the fairness achieved by full fine-tuning.
• Enhanced Fairness Metrics: Development of more nuanced metrics that capture the broad spectrum of biases and toxic behaviors, providing deeper insights into model alignment.
• Robustness Analysis: Exploration of robustness in fine-tuned models to input perturbations, particularly in the context of LoRA, to mitigate unintended consequences.

Conclusion

In conclusion, "Low-rank finetuning for LLMs: A fairness perspective" offers a detailed and critical examination of LoRA methods, highlighting their limitations in ensuring fair and responsible model outputs. The research advocates for careful scrutiny and a balanced approach to leveraging computational efficiency while striving for models that uphold societal values of fairness and neutrality.