21 citations · 62 across the 10 of their papers we have counts for
11 papers
Advancing Speech Language Models by Scaling Supervised Fine-Tuning with Over 60,000 Hours of Synthetic Speech Dialogue Data
Shuaijiang Zhao, Tingwei Guo, Bajian Xiang +4
The GPT-4o represents a significant milestone in enabling real-time interaction with large language models (LLMs) through speech, its remarkable low latency and high fluency not on…
A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model
Xianghui Sun, Yunjie Ji, Baochang Ma +1
Recently, the instruction-tuning of large language models is a crucial area of research in the field of natural language processing. Due to resource and cost limitations, several r…
Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation
Yunjie Ji, Yan Gong, Yong Deng +4
Recently, significant public efforts have been directed towards developing low-cost models with capabilities akin to ChatGPT, thereby fostering the growth of open-source conversati…
Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases
Yunjie Ji, Yong Deng, Yan Gong +5
The success of ChatGPT has recently attracted numerous efforts to replicate it, with instruction-tuning strategies being a key factor in achieving remarkable results. Instruction-t…
Exploring ChatGPT's Ability to Rank Content: A Preliminary Study on Consistency with Human Preferences
Yunjie Ji, Yan Gong, Yiping Peng +5
As a natural language assistant, ChatGPT is capable of performing various tasks, including but not limited to article generation, code completion, and data analysis. Furthermore, C…
Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module
Linzhi Huang, Yulong Li, Hongbo Tian +4
In this paper, we delve into semi-supervised 2D human pose estimation. The previous method ignored two problems: (i) When conducting interactive training between large model and li…