6 citations · 7 across the 13 of their papers we have counts for
5 papers · 1 filter
MUSE: An Interactive Meta-Agent for Understanding and Steering LLM-powered Data Science Systems
Wei-Hao Chen, Weixi Tong, Yuan Tian +2
Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science workflows through natural langua…
ALLOY: Generating Reusable Agent Workflows from User Demonstration
Jiawen Li, Zheng Ning, Yuan Tian +1
Large language models (LLMs) enable end-users to delegate complex tasks to autonomous agents through natural language. However, prompt-based interaction faces critical limitations:…
Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data Annotation
Yuan Tian, Daniel Lee, Fei Wu +5
Text-to-SQL models, which parse natural language (NL) questions to executable SQL queries, are increasingly adopted in real-world applications. However, deploying such models in th…
SQLucid: Grounding Natural Language Database Queries with Interactive Explanations
Yuan Tian, Jonathan K. Kummerfeld, Toby Jia-Jun Li +1
Though recent advances in machine learning have led to significant improvements in natural language interfaces for databases, the accuracy and reliability of these systems remain l…
Insights into Natural Language Database Query Errors: From Attention Misalignment to User Handling Strategies
Zheng Ning, Yuan Tian, Zheng Zhang +2
Querying structured databases with natural language (NL2SQL) has remained a difficult problem for years. Recently, the advancement of machine learning (ML), natural language proces…