16 citations · 22 across the 6 of their papers we have counts for
6 papers
Knowledge Boundary Discovery for Large Language Models
Ziquan Wang, Zhongqi Lu
We propose Knowledge Boundary Discovery (KBD), a reinforcement learning based framework to explore the knowledge boundaries of the Large Language Models (LLMs). We define the knowl…
Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning
Jinglin Liang, Jin Zhong, Hanlin Gu +6
Federated Class Continual Learning (FCCL) merges the challenges of distributed client learning with the need for seamless adaptation to new classes without forgetting old ones. The…
PAS: A Position-Aware Similarity Measurement for Sequential Recommendation
Zijie Zeng, Jing Lin, Weike Pan +2
The common item-based collaborative filtering framework becomes a typical recommendation method when equipped with a certain item-to-item similarity measurement. On one hand, we re…
Integrating User and Agent Models: A Deep Task-Oriented Dialogue System
Weiyan Wang, Yuxiang WU, Yu Zhang +3
Task-oriented dialogue systems can efficiently serve a large number of customers and relieve people from tedious works. However, existing task-oriented dialogue systems depend on h…
Partially Observable Markov Decision Process for Recommender Systems
Zhongqi Lu, Qiang Yang
We report the "Recurrent Deterioration" (RD) phenomenon observed in online recommender systems. The RD phenomenon is reflected by the trend of performance degradation when the reco…
Selective Transfer Learning for Cross Domain Recommendation
Zhongqi Lu, Erheng Zhong, Lili Zhao +3
Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-item preference data. In many real-world applications, preference data are usually…