AIA squared: Attribute-Agnostic Imbalance Augmentation for Subgroup Robustness

arXiv:2608.30297 · cs.CL · Submitted 2026-08-31 · Read on arXiv

cs.CL

Submitted: 2026-08-31

Updated: 2026-08-31

Code: https://github.com/trust-nlp/AIA2-Subgroup-Robustness

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Robustness — 32× in this paper · explained in 5 episodes
• Clustering — 21× in this paper · explained in 2 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Ablation — 14× in this paper · explained in 1 episode
• Data Augmentation — 11× in this paper · explained in 7 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Computational overhead — 3× in this paper · explained in 2 episodes
• Distribution Shift — 3× in this paper · explained in 5 episodes
• Diverse Prototypical Ensembles — 3× in this paper
• F1 Score — 3× in this paper · explained in 3 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Just Train Twice — 3× in this paper
• Adaptation — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adjusted Mutual Information — 1× in this paper
• Domain Shift — 1× in this paper · explained in 4 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• Augmenting Data with Mixup for Sentence Classification: An Empirical Study
• DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
• Domino: Discovering Systematic Errors with Cross-Modal Embeddings
• Modern hierarchical, agglomerative clustering algorithms
• Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
• Qwen3 Technical Report
• Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
• EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
• C-Pack: Packed Resources For General Chinese Embeddings

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