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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
Large language models can treat a trailing user comment as part of pasted text, a phenomenon called absorption. Across 20 models, absorption at a bare newline ranges from 7.7% to 66.7%. Explicit boundaries reduce but do not remove this failure.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.04210
#MachineLearning
Researchers measured how reference choice moves a benchmark score by holding the scored output fixed and exchanging the reference. The score moves by 4.95 F1 points for one output and 2.00 for the other.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.03825
#MachineLearning
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