Hung-yi Lee

Joint Optimization of Tool Creation and Use for Large Language Model Agents

Zhi Rui Tam, Chieh-Yen Lin, Yun-Nung Chen, et al.

Encoder-Side Neuron Identification and Amplification for Acoustic Perception in Large Audio-Language Models

Yu-Han Huang, Chih-Kai Yang, Ke-Han Lu, et al.

BlueMagpie-TTS: A Token-Efficient Tokenizer, Language Model, and TTS for Taiwanese-Accent Code-Switching Speech

Ho Lam Chung, Bo-Xuan Zheng, Cheng-Chieh Huang, et al.

REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing

Cheng-Kang Chou, Ming-To Chuang, Ke-Han Lu, et al.

Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models

Chun-Yi Kuan, Wei-Ping Huang, Hung-yi Lee

All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation

Leonardo Haw-Yang Foo, Chih-Kai Yang, Chen-An Li, et al.

TiCo: Time-Controllable Training for Spoken Dialogue Models

Kai-Wei Chang, Wei-Chih Chen, En-Pei Hu, et al.

How Auditory Knowledge in LLM Backbones Shapes Audio Language Models: A Holistic Evaluation

Ke-Han Lu, Szu-Wei Fu, Chao-Han Huck Yang, et al.

This paper investigates how much knowledge about sounds and audio Large Language Models (LLMs) naturally learn from text-only training, and whether this affects their performance when adapted to handle audio. The researchers test different LLMs in three ways: directly questioning them about audio concepts, having them reason about audio descriptions, and fine-tuning them with audio data. They find that the amount of audio knowledge varies significantly between different LLM families, and importantly, LLMs that show better audio understanding in text-only tests also perform better when actually processing audio.