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Mechanistic interpretability usually studies fully trained models, yet computations can change while a model is still learning. Pythia models pass through an early training window in which accuracy falls below one half.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.04119
#MachineLearning
Activation steering remains new for SpeechLLMs. A proposed training-free Contrastive Activation Addition protocol derives steering vectors for common speech tasks from a small number of labeled utterances.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.04683
#MachineLearning
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