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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 Quantification of Large Language Models using Approximate Bayesian Computation
Mridul Sharma, Adeetya Patel, Zaneta D' Souza +3
Despite their widespread applications, Large Language Models (LLMs) often struggle to express uncertainty, posing a challenge for reliable deployment in high stakes and safety crit…
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…