5 papers
Collapse, Repair, and Drift: Dissecting Fine Tuning Dynamics in Speech Language Models
Yichi Wang, Wangjin Zhou, Yizhou Zhang +4
Supervised fine tuning (SFT) is commonly monitored through training and evaluation loss, but loss does not fully characterize how model functionality evolves along the same traject…
Direct or Mediated? Task-Dependent Audio Information Routing in Large Audio Language Models
Yizhou Zhang, Wangjin Zhou, Xin Gu +6
Large Audio Language Models (LALMs) have demonstrated strong performance across a wide range of audio understanding tasks. However, they are typically evaluated on single, coherent…
Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation
Yizhou Zhang, Wangjin Zhou, Yi Zhao +3
Music aesthetics scoring plays a critical role in applications such as dataset curation, generative model evaluation, and reward modeling for music generation. Recent approaches re…
Rethinking Speech Foundation Model Fine-tuning: Better SFT or Better Match?
Wangjin Zhou, Yizhou Zhang, Yichi Wang +1
Supervised fine-tuning (SFT) is widely used to adapt self-supervised speech representations to downstream classification tasks. Small gains observed under a single pretrained check…
SONAR: Self-Distilled Continual Pre-training for Domain Adaptive Audio Representation
Yizhou Zhang, Yuan Gao, Wangjin Zhou +3
Self-supervised learning (SSL) on large-scale datasets like AudioSet has become the dominant paradigm for audio representation learning. While the continuous influx of new, unlabel…