6 citations · 7 across the 13 of their papers we have counts for
13 papers
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…
ISEE: Interactive Semantic Enrichment for Database Fields
Yuan Tian, Yiru Chen, Rakesh R. Menon +8
LLM-based agents are increasingly being deployed for data-related tasks, including data sense-making, exploration, and retrieval. However, their performance heavily depends on the…
Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization
Long Chen, Ryan Razkenari, Yuxuan Zhou +5
As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for different RAG scenarios evaluati…
TACO: Task-Aware Column Description Generation Using LLMs
Ting Cai, Rakesh R. Menon, Yiru Chen +8
Generating accurate and informative column descriptions (e.g. "membership status of customers" for the column name "cust_mem") is essential for a wide range of downstream NLP tasks…
Enhancing Multi-Agent Communication through Attention Steering with Context Relevance
Hongxiang Zhang, Yuan Tian, Tianyi Zhang
LLM-based multi-agent systems have demonstrated remarkable performance on complex tasks through collaborative reasoning. However, these systems tend to rapidly accumulate extremely…
EvoSchema: Towards Text-to-SQL Robustness Against Schema Evolution
Tianshu Zhang, Kun Qian, Siddhartha Sahai +4
Neural text-to-SQL models, which translate natural language questions (NLQs) into SQL queries given a database schema, have achieved remarkable performance. However, database schem…
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:…
Attention-Aligned Reasoning for Large Language Models
Hongxiang Zhang, Yuan Tian, Tianyi Zhang
Large Language Models (LLMs) tend to generate a long reasoning chain when solving complex tasks. However, as the reasoning chain extends, critical intermediate steps and the origin…
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…
Selective Prompt Anchoring for Code Generation
Yuan Tian, Tianyi Zhang
Recent advances in large language models (LLMs) have transformed software development by automatically generating code from natural language. Yet challenges remain in generating fu…
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…
Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations
Yuan Tian, Zheng Zhang, Zheng Ning +3
Relational databases play an important role in business, science, and more. However, many users cannot fully unleash the analytical power of relational databases, because they are…