11 citations · 28 across the 9 of their papers we have counts for
9 papers
Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction
Xiaoyu Tan, Yongxin Deng, Chao Qu +4
Recently, models for user representation learning have been widely applied in click-through-rate (CTR) and conversion-rate (CVR) prediction. Usually, the model learns a universal u…
CogniDual Framework: Self-Training Large Language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks
Yongxin Deng, Xihe Qiu, Xiaoyu Tan +5
Cognitive psychology investigates perception, attention, memory, language, problem-solving, decision-making, and reasoning. Kahneman's dual-system theory elucidates the human decis…
FedSlate:A Federated Deep Reinforcement Learning Recommender System
Yongxin Deng, Xihe Qiu, Xiaoyu Tan +1
Reinforcement learning methods have been used to optimize long-term user engagement in recommendation systems. However, existing reinforcement learning-based recommendation systems…
Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework
Yongxin Deng, Xihe Qiu, Jue Chen +1
The inherent uncertainty in the environmental transition model of Reinforcement Learning (RL) necessitates a delicate balance between exploration and exploitation. This balance is…
BTBR: A Bayesian-Theory-Driven Probabilistic-Fuzzy Framework for Implicit Bias Removal in Large Language Models
Yongxin Deng, Xihe Qiu, Xiaoyu Tan +8
Large language models (LLMs) may encode biased associations from heterogeneous training corpora that are not immediately visible under ordinary prompting, but can surface when the…
Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Bootstrapped Prolog-based Chain-of-Thought
Xiaoyu Tan, Yongxin Deng, Xihe Qiu +7
Large language models have demonstrated remarkable capabilities as general-purpose assistants, excelling in a wide range of reasoning tasks and supporting various aspects of daily…
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models
Xihe Qiu, Haoyu Wang, Xiaoyu Tan +6
Effective collaboration in multi-agent systems requires communicating goals and intentions between agents. Current agent frameworks often suffer from dependencies on single-agent e…
Struct-X: Enhancing Large Language Models Reasoning with Structured Data
Xiaoyu Tan, Haoyu Wang, Xihe Qiu +4
Structured data, rich in logical and relational information, has the potential to enhance the reasoning abilities of large language models (LLMs). Still, its integration poses a ch…
Subequivariant Reinforcement Learning Framework for Coordinated Motion Control
Haoyu Wang, Xiaoyu Tan, Xihe Qiu +1
Effective coordination is crucial for motion control with reinforcement learning, especially as the complexity of agents and their motions increases. However, many existing methods…