Fairness-Aware Test-Time Prompt Tuning

• URL: http://arxiv.org/abs/2608.25707v1
• Date: Wed, 26 Aug 2026 12:26:18 GMT
• Title: Fairness-Aware Test-Time Prompt Tuning
• Abstract summary: We develop a novel fairness-aware episodic test-time debiasing method called FairTPT.<n>We find that standard episodic TTA generally exacerbates disparities between majority and minority groups.<n>FairTPT outperforms all state-of-the-art episodic test-time debiasing methods and establishes a foundation for robust TTA.
• Score: 4.494887082521271
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Vision-language models have displayed remarkable capabilities in multi-modal understanding and are increasingly used in critical applications where economic and practical deployment constraints prohibit re-training or fine-tuning. However, these models can also exhibit systematic biases that disproportionately affect protected demographic groups and existing approaches to addressing these biases require extensive model retraining and access to demographic attributes. There is a clear need to develop test-time adaptation (TTA) approaches that improve the fairness characteristics of pretrained models under distributional shift. In this paper, we evaluate how episodic TTA affects fairness in CLIP classification under subpopulation shifts and develop FairTPT, a novel fairness-aware episodic TTA method that jointly minimizes target marginal entropy while maximizing spurious marginal entropy through soft-prompt tuning. We find that standard episodic TTA generally exacerbates disparities between majority and minority groups, that blinding a model to spurious attributes without degrading target performance is inherently challenging, and that excessive blinding can lead to catastrophic forgetting. This model collapse can be prevented by monitoring test-time changes in target loss within the linear regime, while still achieving fairness improvements on reactive data and preserving overall performance. FairTPT outperforms all state-of-the-art episodic test-time debiasing methods and establishes a foundation for robust TTA, which is essential for achieving fairness in practice.

Related papers

• Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification [10.816479922364097]
  We propose a general Amortized Predictability-aware Training Framework (APTF) for both time series forecasting (TSF) and time series classification (TSC)<n>APTF introduces two key designs that enable the model to focus on high-predictability samples while still learning appropriately from low-predictability ones.
  arXiv  Detail & Related papers  (2026-02-18T06:59:05Z)
• Fair Context Learning for Evidence-Balanced Test-Time Adaptation in Vision-Language Models [10.45965859391796]
  Test-Time Adaptation (TTA) aims to improve robustness using only unlabeled test samples.<n>Most prompt-based TTA methods rely on entropy minimization.<n>We propose Fair Context Learning (FCL) that avoids entropy minimization by explicitly addressing shared-evidence bias.
  arXiv  Detail & Related papers  (2026-02-02T16:02:50Z)
• Dual-level Modality Debiasing Learning for Unsupervised Visible-Infrared Person Re-Identification [59.59359638389348]
  We propose a Dual-level Modality Debiasing Learning framework that implements debiasing at both the model and optimization levels.<n>Experiments on benchmark datasets demonstrate that DMDL could enable modality-invariant feature learning and a more generalized model.
  arXiv  Detail & Related papers  (2025-12-03T12:43:16Z)
• CLIPTTA: Robust Contrastive Vision-Language Test-Time Adaptation [15.732351927470452]
  Vision-language models (VLMs) like CLIP exhibit strong zero-shot capabilities but often fail to generalize under distribution shifts.<n>Test-time adaptation (TTA) allows models to update at inference time without labeled data, typically via entropy minimization.<n>We propose CLIPTTA, a new gradient-based TTA method for vision-language models that leverages a soft contrastive loss aligned with CLIP's pre-training objective.
  arXiv  Detail & Related papers  (2025-07-18T18:32:17Z)
• R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning [69.72249695674665]
  We propose a robust test-time prompt tuning (R-TPT) for vision-language models (VLMs)<n>R-TPT mitigates the impact of adversarial attacks during the inference stage.<n>We introduce a plug-and-play reliability-based weighted ensembling strategy to strengthen the defense.
  arXiv  Detail & Related papers  (2025-04-15T13:49:31Z)
• Fairness-Aware Low-Rank Adaptation Under Demographic Privacy Constraints [4.647881572951815]
  Pre-trained foundation models can be adapted for specific tasks using Low-Rank Adaptation (LoRA)<n>Existing fairness-aware fine-tuning methods rely on direct access to sensitive attributes or their predictors.<n>We introduce a set of LoRA-based fine-tuning methods that can be trained in a distributed fashion.
  arXiv  Detail & Related papers  (2025-03-07T18:49:57Z)
• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• Identifying and Mitigating Social Bias Knowledge in Language Models [52.52955281662332]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases.<n>FAST surpasses state-of-the-art baselines with superior debiasing performance.<n>This highlights the potential of fine-grained debiasing strategies to achieve fairness in large language models.
  arXiv  Detail & Related papers  (2024-08-07T17:14:58Z)
• Low-rank finetuning for LLMs: A fairness perspective [54.13240282850982]
  Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models. This paper investigates the effectiveness of these methods in capturing the shift of fine-tuning datasets from the initial pre-trained data distribution. We show that low-rank fine-tuning inadvertently preserves undesirable biases and toxic behaviors.
  arXiv  Detail & Related papers  (2024-05-28T20:43:53Z)
• TEA: Test-time Energy Adaptation [67.4574269851666]
  Test-time adaptation (TTA) aims to improve model generalizability when test data diverges from training distribution. We propose a novel energy-based perspective, enhancing the model's perception of target data distributions.
  arXiv  Detail & Related papers  (2023-11-24T10:49:49Z)

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.