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cs.LG2026
Operationalising the Superficial Alignment Hypothesis via Task Complexity
Tomás Vergara-Browne, Darshan Patil, Ivan Titov +3
The superficial alignment hypothesis (SAH) posits that large language models learn most of their knowledge during pre-training, and that post-training merely surfaces this knowledg…
cs.LG2025
Uncertainty-Aware Calibrated Clinical Text Classification with Large Language Models
Mridul Sharma, Adeetya Patel, Zaneta D' Souza +3
Large language models are increasingly used for clinical text classification, where overconfident misclassifications can directly affect patient care. Existing black-box uncertaint…
cs.LG2024
ROSA: Random Subspace Adaptation for Efficient Fine-Tuning
Marawan Gamal Abdel Hameed, Aristides Milios, Siva Reddy +1
Model training requires significantly more memory, compared with inference. Parameter efficient fine-tuning (PEFT) methods provide a means of adapting large models to downstream ta…