inversedMixup: New Data Augmentation Method Combines Mixup Controllability with LLM Interpretability
Researchers propose inversedMixup, a data augmentation framework that combines the controllable mixing of Mixup with the interpretability of language model-based generation by inverting mixed embeddings back into human-readable text. The method aligns a task-specific model's output embeddings with an LLM's input embeddings, allowing mixed samples to be reconstructed as readable sentences while maintaining control over the mixing ratio. This approach provides empirical evidence of manifold intrusion in text Mixup and demonstrates effectiveness in both few-shot and fully supervised learning scenarios.
inversedMixup addresses a key limitation in existing data augmentation techniques for natural language processing. Traditional Mixup generates augmented samples through linear interpolation of inputs and labels at the embedding level, offering fine-grained control but producing non-interpretable outputs. Conversely, LLM-based augmentation methods generate readable text but provide limited control over the generation process. The proposed inversedMixup framework leverages recent advances in LLM inversion—the ability to reconstruct natural language from embeddings—to bridge this gap. By aligning the output embedding space of a task-specific model with the input embedding space of an LLM, the method enables mixed embeddings to be reconstructed into human-interpretable sentences under a controllable mixing ratio. The authors provide the first empirical evidence of the manifold intrusion phenomenon in text Mixup and introduce a three-stage augmentation method with a strategy to mitigate this issue. Extensive experiments validate the approach's effectiveness and generalizability across different learning scenarios.
What's missing
The paper's own limitations and open questions are not detailed in the abstract provided. Specific experimental results (e.g., performance metrics, baseline comparisons, datasets used) are not included in this announcement.
What different sources said
- arXiv cs.CLCenter
inversedMixup: Data Augmentation via Inverting Mixed Embeddings
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