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papersTODAY 04:00 UTC

arXiv Paper Proposes Machine Unlearning for Speech Question Answering Models

A new arXiv preprint examines how large audio-language models can be made to forget sensitive information they may have memorized during training. The work focuses on the speech question-answering setting, where such models have shown strong performance but also carry privacy risks. The authors frame machine unlearning as a way to reduce unintended retention of private data in these systems.

arXiv audio-language-models data privacy machine-unlearning speech-question-answering

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.CLMachine Unlearning for Speech Question Answering in Large Audio-Language Models ↗TODAY 04:00 UTC

arXiv cs.LGMachine Unlearning for Speech Question Answering in Large Audio-Language Models ↗TODAY 04:00 UTC