1 citations · 1 across the 6 of their papers we have counts for
7 papers
AutoSIFT: Automatic Style Sifting for Controllable Speech Generation with Arbitrary Style Infilling
Haowei Lou, Junda Wu, Chengkai Huang +4
State-of-the-art text-to-speech (TTS) models achieve impressive naturalness and expressiveness, yet fine-grained, disentangled control over speaking styles remains challenging. In…
Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction
Hongtao Huang, Chengkai Huang, Junda Wu +3
Forecasting multi-step user behavior trajectories requires reasoning over structured preferences across future actions, a challenge overlooked by traditional sequential recommendat…
Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Ruhan Wang, Zhiyong Wang, Chengkai Huang +5
For question-answering (QA) tasks, in-context learning (ICL) enables language models to generate responses without modifying their parameters by leveraging examples provided in the…
A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms
Chengkai Huang, Hongtao Huang, Tong Yu +6
Recommender systems (RS) have become essential in filtering information and personalizing content for users. RS techniques have traditionally relied on modeling interactions betwee…
DrunkAgent: Stealthy Memory Corruption in LLM-Powered Recommender Agents
Shiyi Yang, Zhibo Hu, Xinshu Li +5
Large language model (LLM)-powered agents are increasingly used in recommender systems (RSs) to achieve personalized behavior modeling, where the memory mechanism plays a pivotal r…
Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models
Chengkai Huang, Yu Xia, Rui Wang +4
Retrieval-augmented large language models (LLMs) have been remarkably competent in various NLP tasks. However, it was observed by previous works that retrieval is not always helpfu…
Diffusion Policies for Risk-Averse Behavior Modeling in Offline Reinforcement Learning
Xiaocong Chen, Siyu Wang, Tong Yu +1
Offline reinforcement learning (RL) presents distinct challenges as it relies solely on observational data. A central concern in this context is ensuring the safety of the learned…