1 citations · 2 across the 11 of their papers we have counts for
5 papers · 1 filter
Video-Text Dataset Construction from Multi-AI Feedback: Promoting Weak-to-Strong Preference Learning for Video Large Language Models
Hao Yi, Qingyang Li, Yulan Hu +3
High-quality video-text preference data is crucial for Multimodal Large Language Models (MLLMs) alignment. However, existing preference data is very scarce. Obtaining VQA preferenc…
Investigating the Synergistic Effects of Dropout and Residual Connections on Language Model Training
Qingyang Li, Weimao Ke
This paper examines the pivotal role of dropout techniques in mitigating overfitting in language model training. It conducts a comprehensive investigation into the influence of var…
TSO: Self-Training with Scaled Preference Optimization
Kaihui Chen, Hao Yi, Qingyang Li +4
Enhancing the conformity of large language models (LLMs) to human preferences remains an ongoing research challenge. Recently, offline approaches such as Direct Preference Optimiza…
Towards Comprehensive Preference Data Collection for Reward Modeling
Yulan Hu, Qingyang Li, Sheng Ouyang +6
Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large language models (LLMs) with human preferences, thereby enhancing the quality of responses gener…
Synthetic Dialogue Dataset Generation using LLM Agents
Yelaman Abdullin, Diego Molla-Aliod, Bahadorreza Ofoghi +2
Linear programming (LP) problems are pervasive in real-life applications. However, despite their apparent simplicity, an untrained user may find it difficult to determine the linea…