On Fairness of Low-Rank Adaptation of Large Models

• URL: http://arxiv.org/abs/2405.17512v2
• Date: Wed, 18 Sep 2024 00:55:35 GMT
• Title: On Fairness of Low-Rank Adaptation of Large Models
• Authors: Zhoujie Ding, Ken Ziyu Liu, Pura Peetathawatchai, Berivan Isik, Sanmi Koyejo,
• Abstract summary: Low-rank adaptation of large models, particularly LoRA, has gained traction due to its computational efficiency. We ask whether LoRA has an unexamined impact on utility, calibration, and resistance to membership inference across different subgroups.
• Score: 14.522061948788863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-rank adaptation of large models, particularly LoRA, has gained traction due to its computational efficiency. This efficiency, contrasted with the prohibitive costs of full-model fine-tuning, means that practitioners often turn to LoRA and sometimes without a complete understanding of its ramifications. In this study, we focus on fairness and ask whether LoRA has an unexamined impact on utility, calibration, and resistance to membership inference across different subgroups (e.g., genders, races, religions) compared to a full-model fine-tuning baseline. We present extensive experiments across vision and language domains and across classification and generation tasks using ViT-Base, Swin-v2-Large, Llama-2 7B, and Mistral 7B. Intriguingly, experiments suggest that while one can isolate cases where LoRA exacerbates model bias across subgroups, the pattern is inconsistent -- in many cases, LoRA has equivalent or even improved fairness compared to the base model or its full fine-tuning baseline. We also examine the complications of evaluating fine-tuning fairness relating to task design and model token bias, calling for more careful fairness evaluations in future work.

Related papers

• The Scaling Law for LoRA Base on Mutual Information Upper Bound [16.527968425791393]
  In fine-tuning, the law among model performance, model parameters, and data complexity has been a focal issue in the field. We propose an internal metric based on the Mutual Information Upper Bound (MIUB) theory to investigate the scaling law of large-model LoRA fine-tuning. The proposed MIUB metric aligns more accurately and stably with the scaling law of LoRA fine-tuning compared to cross-entropy and perplexity.
  arXiv  Detail & Related papers  (2025-01-06T17:19:19Z)
• Evaluating Gender Bias Transfer between Pre-trained and Prompt-Adapted Language Models [4.274270062767065]
  In this work, we investigate the bias transfer hypothesis (BTH) under prompt adaptations. We find that bias transfer remains strongly correlated even when LLMs are specifically prompted to exhibit fair or biased behavior. Our findings highlight the importance of ensuring fairness in pre-trained LLMs.
  arXiv  Detail & Related papers  (2024-12-04T18:32:42Z)
• LoRA vs Full Fine-tuning: An Illusion of Equivalence [76.11938177294178]
  We study how different fine-tuning methods change pre-trained models by analyzing the model's weight matrices through the lens of their spectral properties. We find that full fine-tuning and LoRA yield weight matrices whose singular value decompositions exhibit very different structure. We conclude by examining why intruder dimensions appear in LoRA fine-tuned models, why they are undesirable, and how their effects can be minimized.
  arXiv  Detail & Related papers  (2024-10-28T17:14:01Z)
• FairLoRA: Unpacking Bias Mitigation in Vision Models with Fairness-Driven Low-Rank Adaptation [3.959853359438669]
  We introduce FairLoRA, a novel fairness-specific regularizer for Low Rank Adaptation (LoRA) Our results demonstrate that the need for higher ranks to mitigate bias is not universal; it depends on factors such as the pre-trained model, dataset, and task.
  arXiv  Detail & Related papers  (2024-10-22T18:50:36Z)
• Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation [58.288682735160585]
  Low-Rank Adaptation (LoRA) is a popular technique for finetuning models. LoRA often under performs when compared to full- parameter fine-tuning. We present a framework that rigorously analyzes the adaptation rates of LoRA methods.
  arXiv  Detail & Related papers  (2024-10-10T18:51:53Z)
• AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models [0.9514837871243403]
  Low-rank adaptation (LoRA) is a fine-tuning technique that can be applied to conditional generative diffusion models. We introduce AutoLoRA, a novel guidance technique for diffusion models fine-tuned with the LoRA approach.
  arXiv  Detail & Related papers  (2024-10-04T21:57:11Z)
• Mixture of LoRA Experts [87.50120181861362]
  This paper introduces the Mixture of LoRA Experts (MoLE) approach, which harnesses hierarchical control and unfettered branch selection. The MoLE approach achieves superior LoRA fusion performance in comparison to direct arithmetic merging.
  arXiv  Detail & Related papers  (2024-04-21T11:59:53Z)
• DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision [73.80009454050858]
  This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Our model jointly optimize for two fairness criteria - group fairness and counterfactual fairness.
  arXiv  Detail & Related papers  (2023-03-15T07:13:54Z)
• FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes [51.02407217197623]
  We propose a two-stage training algorithm named FAIRIF. It minimizes the loss over the reweighted data set where the sample weights are computed. We show that FAIRIF yields models with better fairness-utility trade-offs against various types of bias.
  arXiv  Detail & Related papers  (2022-01-15T05:14:48Z)
• Characterizing Fairness Over the Set of Good Models Under Selective Labels [69.64662540443162]
  We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance. We provide tractable algorithms to compute the range of attainable group-level predictive disparities. We extend our framework to address the empirically relevant challenge of selectively labelled data.
  arXiv  Detail & Related papers  (2021-01-02T02:11:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.