10 papers
Towards Root Memories: Benchmarking and Enhancing Implicit Logical Memory Retrieval for Personalized LLMs
Hongxun Ding, Xiang Yu, Chengbing Wang +4
Memory systems are essential for personalized Large Language Models (LLMs). However, existing retrieval methods in these systems primarily rely on semantic similarity, potentially…
From Empathy to Personalized Empathy: Adapting Empathetic Strategies to Individual Users
Wuqiang Zheng, Chengbing Wang, Yilin Yang +6
As Large Language Models (LLMs) are increasingly deployed in long-term interactions with users, empathy has become an increasingly important capability. However, existing research…
Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
Bohao Wang, Yu Cui, Zhenxiang Xu +13
The field of recommender systems (RS) is currently undergoing two profound paradigm shifts. From the perspective of objectives, the goal has shifted beyond mere recommendation accu…
NextQuill: Causal Preference Modeling for Enhancing LLM Personalization
Xiaoyan Zhao, Juntao You, Yang Zhang +5
Personalizing large language models (LLMs) for individual users has become increasingly important as they are progressively integrated into real-world applications to support users…
Think-While-Generating: On-the-Fly Reasoning for Personalized Long-Form Generation
Chengbing Wang, Yang Zhang, Wenjie Wang +4
Preference alignment has enabled large language models (LLMs) to better reflect human expectations, but current methods mostly optimize for population-level preferences, overlookin…
PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models
Chengbing Wang, Wuqiang Zheng, Yang Zhang +5
Large Language Models (LLMs) are increasingly deployed in human-centric applications, yet they often fail to provide substantive emotional support. While Reinforcement Learning (RL…