5 citations · 9 across the 5 of their papers we have counts for
7 papers
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…
Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction
Zhixuan Chu, Mengxuan Hu, Qing Cui +2
Since artificial intelligence has seen tremendous recent successes in many areas, it has sparked great interest in its potential for trustworthy and interpretable risk prediction.…
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…
Intelligent Virtual Assistants with LLM-based Process Automation
Yanchu Guan, Dong Wang, Zhixuan Chu +6
While intelligent virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous in modern life, they still face limitations in their ability to follow multi-step…
Data-Centric Financial Large Language Models
Zhixuan Chu, Huaiyu Guo, Xinyuan Zhou +9
Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance. LLMs have difficulty reasoning about and in…