1 citations · 1 across the 10 of their papers we have counts for
11 papers
How Auditory Knowledge in LLM Backbones Shapes Audio Language Models: A Holistic Evaluation
Ke-Han Lu, Szu-Wei Fu, Chao-Han Huck Yang +13
Large language models (LLMs) have been widely used as knowledge backbones of Large Audio Language Models (LALMs), yet how much auditory knowledge they encode through text-only pre-…
SPAR-K: Scheduled Periodic Alternating Early Exit for Spoken Language Models
Hsiao-Ying Huang, Cheng-Han Chiang, Hung-yi Lee
Interleaved spoken language models (SLMs) alternately generate text and speech tokens, but decoding at full transformer depth for every step becomes costly, especially due to long…
Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Models
Yu-Xiang Lin, Cheng-Han Chiang, Hung-yi Lee
In this paper, we show that when spoken language models (SLMs) are instructed to speak in a specific speaking style at the beginning of a multi-turn conversation, they cannot maint…
SHANKS: Simultaneous Hearing and Thinking for Spoken Language Models
Cheng-Han Chiang, Xiaofei Wang, Linjie Li +7
Current large language models (LLMs) and spoken language models (SLMs) begin thinking and taking actions only after the user has finished their turn. This prevents the model from i…
STITCH: Simultaneous Thinking and Talking with Chunked Reasoning for Spoken Language Models
Cheng-Han Chiang, Xiaofei Wang, Linjie Li +7
Spoken Language Models (SLMs) are designed to take speech inputs and produce spoken responses. However, current SLMs lack the ability to perform an internal, unspoken thinking proc…
DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment
Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu +25
We introduce DeSTA2.5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following. Recent LALMs augment Large Langu…
Audio-Aware Large Language Models as Judges for Speaking Styles
Cheng-Han Chiang, Xiaofei Wang, Chung-Ching Lin +8
Audio-aware large language models (ALLMs) can understand the textual and non-textual information in the audio input. In this paper, we explore using ALLMs as an automatic judge to…
Reducing Object Hallucination in Large Audio-Language Models via Audio-Aware Decoding
Tzu-wen Hsu, Ke-Han Lu, Cheng-Han Chiang +1
Large Audio-Language Models (LALMs) can take audio and text as the inputs and answer questions about the audio. While prior LALMs have shown strong performance on standard benchmar…
TRACT: Regression-Aware Fine-tuning Meets Chain-of-Thought Reasoning for LLM-as-a-Judge
Cheng-Han Chiang, Hung-yi Lee, Michal Lukasik
The LLM-as-a-judge paradigm uses large language models (LLMs) for automated text evaluation, where a numerical assessment is assigned by an LLM to the input text following scoring…
Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course
Cheng-Han Chiang, Wei-Chih Chen, Chun-Yi Kuan +2
Using large language models (LLMs) for automatic evaluation has become an important evaluation method in NLP research. However, it is unclear whether these LLM-based evaluators can…
REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR
Liang-Hsuan Tseng, En-Pei Hu, Cheng-Han Chiang +4
Unsupervised automatic speech recognition (ASR) aims to learn the mapping between the speech signal and its corresponding textual transcription without the supervision of paired sp…