Counterfactual prompting effects on LLMs are often indistinguishable from those caused by meaning-preserving paraphrases, causing most previously reported demographic sensitivities to disappear under proper statistical comparison.
M., & Khoshgoftaar, T
5 Pith papers cite this work. Polarity classification is still indexing.
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Class imbalance causes DNNs to underfit minority classes early in training and produce non-generalizable minority representations later by overfitting to minimize overall loss.
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Mistake gating leads to energy and memory efficient continual learning
Mistake-gated plasticity reduces neural network updates by 50-80% by gating changes on classification errors, improving efficiency for continual learning without added hyperparameters.