发表机构
University of Memphis; University of Missouri（孟菲斯大学; 密苏里大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出AIA²框架，通过潜在语义分布和LLM实现无明确子群体标注的子群体不平衡增强，在5个语料库上提升了模型最差子群体性能与鲁棒性。
AI 中文摘要
描述数据内容和上下文的属性会引发超出标签不平衡的多样不平衡模式。然而现有研究主要处理标签不平衡，却忽略了主题、人口统计等数据属性，这些属性会形成有意义的子群体结构，导致模型在代表性不足的子群体上性能下降。本文提出属性无关不平衡增强（Attribute-Agnostic Imbalance Augmentation，AIA²）框架，用于在无明确子群体标注的情况下提升模型在不同子群体不平衡下的鲁棒性。AIA²通过潜在语义分布自动发现不同的不平衡情况，获取兼具学习难度和子群体不平衡缺陷的切片，并利用大语言模型（Large Language Model，LLM）进行子群体感知的不平衡增强。我们在5个涵盖丰富领域及属性值、涉及社会问题和多样主题的通用语料库上评估AIA²，结果显示其在性能最差的子群体上表现提升，且相比竞争性基准方法取得持续增益。消融研究证实各组件的互补贡献，额外分析表明AIA²为改善数据子群体不平衡下的最差群体鲁棒性提供了实用且一致的方法。代码可在指定URL获取。
英文摘要
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.
