Model-Agnostic Meta Learning for Class Imbalance Adaptation

2026-04-22 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Natural Language Processing · Depth: Expert, extended

Summary

The Hardness-Aware Meta-Resample (HAMR) framework, developed by researchers at the University of Memphis, addresses class imbalance and data difficulty in Natural Language Processing (NLP) tasks. HAMR employs bi-level optimization to dynamically estimate instance-level weights, prioritizing challenging samples and minority classes. It also uses a neighborhood-aware resampling mechanism to amplify training focus on hard examples and their semantically similar neighbors. Validated on six imbalanced datasets across biomedical, disaster response, and sentiment domains, HAMR consistently outperforms strong baselines, showing substantial improvements for minority classes. Ablation studies confirm that its adaptive weighting and neighborhood-based resampling modules synergistically contribute to performance gains, making HAMR a flexible and generalizable approach for class imbalance adaptation.

Key takeaway

For AI Engineers and Research Scientists developing NLP models for imbalanced datasets, HAMR offers a robust solution to improve minority class performance without sacrificing overall accuracy. You should consider integrating HAMR's adaptive weighting and neighborhood-aware resampling, especially for tasks like Named Entity Recognition, to achieve more consistent and significant gains compared to traditional reweighting or sampling methods. This approach is particularly effective in scenarios with high imbalance ratios, ensuring your models learn from critical, rare examples.

Key insights

HAMR dynamically addresses class imbalance and data difficulty in NLP via adaptive weighting and neighborhood-aware resampling.

Principles

• Example difficulty is not uniform within classes.
• Dynamic weighting outperforms static heuristics.
• Neighborhood expansion improves resampling robustness.

Method

HAMR uses bi-level meta-optimization for adaptive weight estimation and hardness-aware region resampling. It calculates instance importance and reshapes training distribution by focusing on challenging semantic regions, updated periodically via KNN on embeddings.

In practice

• Apply HAMR to NLP tasks with severe class imbalance.
• Consider encoder-based models for structured prediction.
• Use FAISS for efficient similarity search in resampling.

Topics

• Class Imbalance
• Meta-Learning
• Adaptive Weighting
• Hardness-Aware Resampling
• Natural Language Processing

Code references

• trust-nlp/ImbalanceLearning

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.