12 citations · 27 across the 12 of their papers we have counts for
6 papers · 1 filter
MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time
Mozhi Zhang, Pengyu Wang, Chenkun Tan +4
Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora, making them powerful tools for various applications. To make LLMs mor…
IntrinsicVoice: Empowering LLMs with Intrinsic Real-time Voice Interaction Abilities
Xin Zhang, Xiang Lyu, Zhihao Du +10
Current methods of building LLMs with voice interaction capabilities rely heavily on explicit text autoregressive generation before or during speech response generation to maintain…
SpeechAlign: Aligning Speech Generation to Human Preferences
Dong Zhang, Zhaowei Li, Shimin Li +4
Speech language models have significantly advanced in generating realistic speech, with neural codec language models standing out. However, the integration of human feedback to ali…
Calibrating the Confidence of Large Language Models by Eliciting Fidelity
Mozhi Zhang, Mianqiu Huang, Rundong Shi +5
Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless. However, post-alignment, these language models often exhibit o…
SpeechGPT-Gen: Scaling Chain-of-Information Speech Generation
Dong Zhang, Xin Zhang, Jun Zhan +3
Benefiting from effective speech modeling, current Speech Large Language Models (SLLMs) have demonstrated exceptional capabilities in in-context speech generation and efficient gen…
SpeechAgents: Human-Communication Simulation with Multi-Modal Multi-Agent Systems
Dong Zhang, Zhaowei Li, Pengyu Wang +3
Human communication is a complex and diverse process that not only involves multiple factors such as language, commonsense, and cultural backgrounds but also requires the participa…