The test error of random-feature ridge regression with arbitrary data augmentation admits a closed-form asymptotic characterization in the proportional regime that depends only on population covariances and augmentation statistics.
A survey of data augmentation approaches for NLP
3 Pith papers cite this work. Polarity classification is still indexing.
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AppRay integrates LLM-guided task-oriented exploration with a contrastive learning multi-label classifier and rule-based refiner to detect intra- and inter-page dark patterns, reporting 0.89/0.85 F1 on new datasets with large gains over prior methods.
TDG uses GPT-4 to generate meta-templates that synthesize over 7 million verifiable grade school math problems for training and aligning LLMs on reasoning tasks.
citing papers explorer
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Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation
The test error of random-feature ridge regression with arbitrary data augmentation admits a closed-form asymptotic characterization in the proportional regime that depends only on population covariances and augmentation statistics.
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From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps
AppRay integrates LLM-guided task-oriented exploration with a contrastive learning multi-label classifier and rule-based refiner to detect intra- and inter-page dark patterns, reporting 0.89/0.85 F1 on new datasets with large gains over prior methods.
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Training and Evaluating Language Models with Template-based Data Generation
TDG uses GPT-4 to generate meta-templates that synthesize over 7 million verifiable grade school math problems for training and aligning LLMs on reasoning tasks.