24 citations · 41 across the 7 of their papers we have counts for
10 papers
Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration
Yanchu Guan, Dong Wang, Yan Wang +5
Autonomous mobile app interaction has become increasingly important with growing complexity of mobile applications. Developing intelligent agents that can effectively navigate and…
A Causal Explainable Guardrails for Large Language Models
Zhixuan Chu, Yan Wang, Longfei Li +3
Large Language Models (LLMs) have shown impressive performance in natural language tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for steer…
Professional Agents -- Evolving Large Language Models into Autonomous Experts with Human-Level Competencies
Zhixuan Chu, Yan Wang, Feng Zhu +3
The advent of large language models (LLMs) such as ChatGPT, PaLM, and GPT-4 has catalyzed remarkable advances in natural language processing, demonstrating human-like language flue…
LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation
Zhixuan Chu, Yan Wang, Qing Cui +4
As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully captu…
Prompt-augmented Temporal Point Process for Streaming Event Sequence
Siqiao Xue, Yan Wang, Zhixuan Chu +7
Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling continuous-time event sequences, such as user activities on the web and financial transactions. In re…
Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference
Yan Wang, Zhixuan Chu, Tao Zhou +9
Asynchronous time series, also known as temporal event sequences, are the basis of many applications throughout different industries. Temporal point processes(TPPs) are the standar…