3 citations · 3 across the 5 of their papers we have counts for
6 papers
Dyad: Extending Large Language Models with Native Typed Decision-Making
Yundaichuan Zhan, Weishi Wang, Wenbiao Liu +5
We study how to build more capable general-purpose agents by extending large language models (LLMs) with native typed decision-making. We introduce Dyad, an architecture that augme…
Decoupling Conversational Dynamics in Full-Duplex Spoken Models through Reinforcement Learning
Yuxin Li, Donghang Wu, Guan-Ting Lin +4
Recent full-duplex spoken dialogue models have demonstrated compelling progress toward human-like interaction, enabling agents to respond with low latency, produce backchannels, an…
Beyond SFT-to-RL: Pre-alignment via Black-Box On-Policy Distillation for Multimodal RL
Sudong Wang, Weiquan Huang, Xiaomin Yu +9
The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiab…
Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models
Yuchen Hu, Chen Chen, Chao-Han Huck Yang +4
We propose an unsupervised adaptation framework, Self-TAught Recognizer (STAR), which leverages unlabeled data to enhance the robustness of automatic speech recognition (ASR) syste…
Overcoming Catastrophic Forgetting by Exemplar Selection in Task-oriented Dialogue System
Chen Chen, Ruizhe Li, Yuchen Hu +3
Intelligent task-oriented dialogue systems (ToDs) are expected to continuously acquire new knowledge, also known as Continual Learning (CL), which is crucial to fit ever-changing u…
Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models
Yuchen Hu, Chen Chen, Chengwei Qin +3
Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR), which aims to predict the ground-truth trans…