Discovering the Hidden Role of Gini Index In Prompt-based Classification

• URL: http://arxiv.org/abs/2603.15654v1
• Date: Sat, 07 Mar 2026 01:39:22 GMT
• Title: Discovering the Hidden Role of Gini Index In Prompt-based Classification
• Abstract summary: We pursue a foundational understanding of the hidden role of Gini Index as a tool for detecting and optimizing (debiasing) disparities in class accuracy.<n>We introduce the intuitions, benchmark Gini scores in real-world LLMs and vision models, and thoroughly discuss the insights of Gini not only as a measure of relative accuracy dominance but also as a direct optimization metric.
• Score: 4.501091570166657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In classification tasks, the long-tailed minority classes usually offer the predictions that are most important. Yet these classes consistently exhibit low accuracies, whereas a few high-performing classes dominate the game. We pursue a foundational understanding of the hidden role of Gini Index as a tool for detecting and optimizing (debiasing) disparities in class accuracy, focusing on the case of prompt-based classification. We introduce the intuitions, benchmark Gini scores in real-world LLMs and vision models, and thoroughly discuss the insights of Gini not only as a measure of relative accuracy dominance but also as a direct optimization metric. Through rigorous case analyses, we first show that weak to strong relative accuracy imbalance exists in both prompt-based, text and image classification results and regardless of whether the classification is high-dimensional or low-dimensional. Then, we harness the Gini metric to propose a post-hoc model-agnostic bias mitigation method. Experimental results across few-shot news, biomedical, and zero-shot image classification show that our method significantly reduces both relative and absolute accuracy imbalances, minimizing top class relative dominance while elevating weakest classes.

Related papers

• Reducing Class-Wise Performance Disparity via Margin Regularization [82.81746960548382]
  Deep neural networks often exhibit substantial disparities in class-wise accuracy, even when trained on class-balanced data.<n>We present Margin Regularization for Performance Disparity Reduction (MR$2$), a theoretically principled regularization for classification.<n>Our analysis reveals how per-class feature variability contributes to error, motivating the use of larger margins for hard classes.
  arXiv  Detail & Related papers  (2026-01-30T12:56:08Z)
• Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference [10.457756074328664]
  This study proposes a method for imbalanced data classification based on deep probabilistic graphical models (DPGMs)<n>We introduce variational inference optimization probability modeling, which enables the model to adaptively adjust the representation ability of minority classes.<n>We combine the adversarial learning mechanism to generate minority class samples in the latent space so that the model can better characterize the category boundary.
  arXiv  Detail & Related papers  (2025-04-08T07:38:30Z)
• Ensemble Debiasing Across Class and Sample Levels for Fairer Prompting Accuracy [17.610305828703957]
  Language models are strong few-shot learners and achieve good overall accuracy in text classification tasks.<n>We believe that the pursuit of overall accuracy should not come from enriching the strong classes, but from raising up the weak ones.<n>We propose a Heaviside step function based ensemble debiasing method, which enables flexible rectifications of in-context learned class probabilities.
  arXiv  Detail & Related papers  (2025-03-07T05:34:31Z)
• Classes Are Not Equal: An Empirical Study on Image Recognition Fairness [100.36114135663836]
  We experimentally demonstrate that classes are not equal and the fairness issue is prevalent for image classification models across various datasets. Our findings reveal that models tend to exhibit greater prediction biases for classes that are more challenging to recognize. Data augmentation and representation learning algorithms improve overall performance by promoting fairness to some degree in image classification.
  arXiv  Detail & Related papers  (2024-02-28T07:54:50Z)
• Bias Mitigating Few-Shot Class-Incremental Learning [17.185744533050116]
  Few-shot class-incremental learning aims at recognizing novel classes continually with limited novel class samples. Recent methods somewhat alleviate the accuracy imbalance between base and incremental classes by fine-tuning the feature extractor in the incremental sessions. We propose a novel method to mitigate model bias of the FSCIL problem during training and inference processes.
  arXiv  Detail & Related papers  (2024-02-01T10:37:41Z)
• Understanding the Detrimental Class-level Effects of Data Augmentation [63.1733767714073]
  achieving optimal average accuracy comes at the cost of significantly hurting individual class accuracy by as much as 20% on ImageNet. We present a framework for understanding how DA interacts with class-level learning dynamics. We show that simple class-conditional augmentation strategies improve performance on the negatively affected classes.
  arXiv  Detail & Related papers  (2023-12-07T18:37:43Z)
• A Saliency-based Clustering Framework for Identifying Aberrant Predictions [49.1574468325115]
  We introduce the concept of aberrant predictions, emphasizing that the nature of classification errors is as critical as their frequency. We propose a novel, efficient training methodology aimed at both reducing the misclassification rate and discerning aberrant predictions. We apply this methodology to the less-explored domain of veterinary radiology, where the stakes are high but have not been as extensively studied compared to human medicine.
  arXiv  Detail & Related papers  (2023-11-11T01:53:59Z)
• Class-Imbalanced Graph Learning without Class Rebalancing [62.1368829847041]
  Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models. In this work, we approach the root cause of class-imbalance bias from an topological paradigm. We devise a lightweight topological augmentation framework BAT to mitigate the class-imbalance bias without class rebalancing.
  arXiv  Detail & Related papers  (2023-08-27T19:01:29Z)
• Test-Time Amendment with a Coarse Classifier for Fine-Grained Classification [10.719054378755981]
  We present a novel approach for Post-Hoc Correction called Hierarchical Ensembles (HiE) HiE utilizes label hierarchy to improve the performance of fine-grained classification at test-time using the coarse-grained predictions. Our approach brings notable gains in top-1 accuracy while significantly decreasing the severity of mistakes as training data decreases for the fine-grained classes.
  arXiv  Detail & Related papers  (2023-02-01T10:55:27Z)
• Parametric Classification for Generalized Category Discovery: A Baseline Study [70.73212959385387]
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
  arXiv  Detail & Related papers  (2022-11-21T18:47:11Z)
• M2m: Imbalanced Classification via Major-to-minor Translation [79.09018382489506]
  In most real-world scenarios, labeled training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion. In this paper, we explore a novel yet simple way to alleviate this issue by augmenting less-frequent classes via translating samples from more-frequent classes. Our experimental results on a variety of class-imbalanced datasets show that the proposed method improves the generalization on minority classes significantly compared to other existing re-sampling or re-weighting methods.
  arXiv  Detail & Related papers  (2020-04-01T13:21:17Z)

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.