4 citations · 11 across the 12 of their papers we have counts for
12 papers
Qwen-Audio-3.1-Realtime: Towards Reliable Agentic Voice Interaction
Lujia Bao, Qian Chen, Luyao Cheng +15
Real-time voice assistants must reason over evolving requests, execute actions, and follow conversational rules. Qwen-Audio-3.1-Realtime brings these requirements together through…
ConsensusBench: Benchmark of Consensus Nodes for LLM Reasoning via Outcome Reward Densifying
Shi-Qi Yan, Chao-Hong Tan, Qian Chen +3
Reinforcement learning (RL) has become one of the primary paradigms for reasoning enhancement of large language models (LLMs). In particular, Group Relative Policy Optimization (GR…
Short Horizons and Sparse Concepts: a Mathematical View of the Readout in the J-lens
Shi-Qi Yan, Kai-Xuan Ding, Chao-Hong Tan +4
The Jacobian lens (J-lens) has been proposed as a way to read verbalizable representations from language models. However, its principle and meaning lack a detailed and theoretical…
Efficient Chain-of-Modality Reasoning via Progressive Compression for Spoken Language Models
Pengchao Feng, Chao-Hong Tan, Qian Chen +3
Spoken language models (SLMs) enable natural human-computer interaction, but their reasoning ability still lags behind that of text-based large language models, especially on spoke…
Is ChatGPT a Good Multi-Party Conversation Solver?
Chao-Hong Tan, Jia-Chen Gu, Zhen-Hua Ling
Large Language Models (LLMs) have emerged as influential instruments within the realm of natural language processing; nevertheless, their capacity to handle multi-party conversatio…
DiffuSIA: A Spiral Interaction Architecture for Encoder-Decoder Text Diffusion
Chao-Hong Tan, Jia-Chen Gu, Zhen-Hua Ling
Diffusion models have emerged as the new state-of-the-art family of deep generative models, and their promising potentials for text generation have recently attracted increasing at…
MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation
Jia-Chen Gu, Chao-Hong Tan, Caiyuan Chu +4
Modeling multi-party conversations (MPCs) with graph neural networks has been proven effective at capturing complicated and graphical information flows. However, existing methods r…
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
Chao-Hong Tan, Jia-Chen Gu, Chongyang Tao +5
Generating natural and informative texts has been a long-standing problem in NLP. Much effort has been dedicated into incorporating pre-trained language models (PLMs) with various…
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations
Jia-Chen Gu, Chao-Hong Tan, Chongyang Tao +4
Recently, various response generation models for two-party conversations have achieved impressive improvements, but less effort has been paid to multi-party conversations (MPCs) wh…
Neural Grapheme-to-Phoneme Conversion with Pre-trained Grapheme Models
Lu Dong, Zhi-Qiang Guo, Chao-Hong Tan +3
Neural network models have achieved state-of-the-art performance on grapheme-to-phoneme (G2P) conversion. However, their performance relies on large-scale pronunciation dictionarie…
PoNet: Pooling Network for Efficient Token Mixing in Long Sequences
Chao-Hong Tan, Qian Chen, Wen Wang +3
Transformer-based models have achieved great success in various NLP, vision, and speech tasks. However, the core of Transformer, the self-attention mechanism, has a quadratic time…
Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems
Chao-Hong Tan, Xiaoyu Yang, Zi'ou Zheng +7
Task-oriented conversational modeling with unstructured knowledge access, as track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to build a system to genera…