AIA$^{2}$: Attribute-Agnostic Imbalance Augmentation for Subgroup Robustness

2026-08-31

AI Summary

本文提出AIA²框架，通过自动发现不平衡并使用大语言模型增强数据，解决属性导致的子群不平衡问题，提高模型鲁棒性。

Abstract

Attributes describing data content and context can induce diverse imbalance patterns that go beyond label imbalance alone. However, existing studies primarily address label imbalance while overlooking data attributes, such as topics and demographics, which can induce meaningful subgroup structure while causing model degradation on underrepresented subgroups. We propose Attribute-Agnostic Imbalance Augmentation (AIA$^{2}$), a framework for improving model robustness under varying subgroup imbalances without explicit subgroup annotations. AIA$^{2}$ automatically discovers varying imbalances via latent semantic distributions, obtains slices with both learning difficulty and subgroup imbalance deficits, and deploys a large language model (LLM) for subgroup-aware imbalance augmentation. We have evaluated AIA$^{2}$ on 5 popular corpora with rich domains and their attribute values, covering social issues and diverse topics. Results show improved performance on the lowest-performing subgroups and consistent gains over competitive baselines. Ablation studies confirm complementary contributions from each component, and additional analyses show that AIA$^{2}$ provides a practical and consistent way to improve worst-group robustness under data subgroup imbalance. Code is available at https://github.com/trust-nlp/AIA2-Subgroup-Robustness.

Problem

• subgroup imbalance
• model robustness
• data attributes

Innovation

• Attribute-Agnostic Imbalance Augmentation
• subgroup robustness
• latent semantic distributions
• large language model