5 citations · 5 across the 2 of their papers we have counts for
5 papers
StyleChat: Learning Recitation-Augmented Memory in LLMs for Stylized Dialogue Generation
Jinpeng Li, Zekai Zhang, Quan Tu +3
Large Language Models (LLMs) demonstrate superior performance in generative scenarios and have attracted widespread attention. Among them, stylized dialogue generation is essential…
Generative News Recommendation
Shen Gao, Jiabao Fang, Quan Tu +4
Most existing news recommendation methods tackle this task by conducting semantic matching between candidate news and user representation produced by historical clicked news. Howev…
CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation
Quan Tu, Shilong Fan, Zihang Tian +1
Recently, the advent of large language models (LLMs) has revolutionized generative agents. Among them, Role-Playing Conversational Agents (RPCAs) attract considerable attention due…
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment
Jixiang Hong, Quan Tu, Changyu Chen +3
Language models trained on large-scale corpus often generate content that is harmful, toxic, or contrary to human preferences, making their alignment with human values a critical c…
CharacterChat: Learning towards Conversational AI with Personalized Social Support
Quan Tu, Chuanqi Chen, Jinpeng Li +5
In our modern, fast-paced, and interconnected world, the importance of mental well-being has grown into a matter of great urgency. However, traditional methods such as Emotional Su…