3 papers
cs.SD2026
Exploring Fine-Tuning of Large Audio Language Models for Spoken Language Understanding under Limited Speech Data
Youngwon Choi, Jaeyoon Jung, Hyeonyu Kim +2
Large Audio Language Models (LALMs) have emerged as powerful tools for speech-related tasks but remain underexplored for fine-tuning, especially with limited speech data. To bridge…
cs.CL2026
MixtureVitae: Open Web-Scale Pretraining Dataset With High Quality Instruction and Reasoning Data Built from Permissive-First Text Sources
Huu Nguyen, Victor May, Harsh Raj +14
We present MixtureVitae, an open-access pretraining corpus built to minimize legal risk while providing strong downstream performance. MixtureVitae follows a permissive-first, risk…
cs.CL2024
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code
Taishi Nakamura, Mayank Mishra, Simone Tedeschi +42
Pretrained language models are an integral part of AI applications, but their high computational cost for training limits accessibility. Initiatives such as Bloom and StarCoder aim…