3 citations · 3 across the 4 of their papers we have counts for
4 papers
ComplexFuncBench: Exploring Multi-Step and Constrained Function Calling under Long-Context Scenario
Lucen Zhong, Zhengxiao Du, Xiaohan Zhang +2
Enhancing large language models (LLMs) with real-time APIs can help generate more accurate and up-to-date responses. However, evaluating the function calling abilities of LLMs in r…
Does RLHF Scale? Exploring the Impacts From Data, Model, and Method
Zhenyu Hou, Pengfan Du, Yilin Niu +7
This study explores the scaling properties of Reinforcement Learning from Human Feedback (RLHF) in Large Language Models (LLMs). Although RLHF is considered an important step in po…
GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot
Aohan Zeng, Zhengxiao Du, Mingdao Liu +5
We introduce GLM-4-Voice, an intelligent and human-like end-to-end spoken chatbot. It supports both Chinese and English, engages in real-time voice conversations, and varies vocal…
Scaling Speech-Text Pre-training with Synthetic Interleaved Data
Aohan Zeng, Zhengxiao Du, Mingdao Liu +4
Speech language models (SpeechLMs) accept speech input and produce speech output, allowing for more natural human-computer interaction compared to text-based large language models…