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cs.CL2026
Adapting Text LLMs to Speech via Multimodal Depth Up-Scaling
Kazuki Yano, Jun Suzuki, Shinji Watanabe
Adapting pre-trained text Large Language Models (LLMs) into Speech Language Models (Speech LMs) via continual pretraining on speech data is promising, but often degrades the origin…
cs.CL2026
Pre-training LLM without Learning Rate Decay Enhances Supervised Fine-Tuning
Kazuki Yano, Shun Kiyono, Sosuke Kobayashi +2
We investigate the role of learning rate scheduling in the large-scale pre-training of large language models, focusing on its influence on downstream performance after supervised f…