2 citations · 5 across the 9 of their papers we have counts for
6 papers · 1 filter
DA-Studio: An Agentic System for End-to-End Data Analysis
Yizhe Liu, Shaolei Zhang, Ju Fan
Real-world data analysis is a multi-step process over heterogeneous inputs rather than merely producing a final answer. A practical system should autonomously organize multi-step w…
TACO: A Benchmark for Open-Domain Text-to-SQL with Ambiguous and Cross-Database Queries
Chao Deng, Ju Fan, Yuyu Luo +7
Text-to-SQL aims to translate natural language questions into executable SQL queries over structured databases. Existing benchmarks mainly focus on closed-domain settings with pred…
DataEvolver: Automatic Data Preparation for Large Language Models through Multi-Level Self-Evolving
Chao Deng, Shaolei Zhang, Ju Fan +1
High-quality training data is essential to large language models (LLMs) and typically requires extensive and costly manual curation. Existing automatic data preparation methods rel…
DeepPrep: An LLM-Powered Agentic System for Autonomous Data Preparation
Meihao Fan, Ju Fan, Yuxin Zhang +7
Data preparation, which aims to transform heterogeneous and noisy raw tables into analysis-ready data, remains a major bottleneck in data science. Recent approaches leverage large…
Automatic Database Configuration Debugging using Retrieval-Augmented Language Models
Sibei Chen, Ju Fan, Bin Wu +8
Database management system (DBMS) configuration debugging, e.g., diagnosing poorly configured DBMS knobs and generating troubleshooting recommendations, is crucial in optimizing DB…
A Plug-and-Play Natural Language Rewriter for Natural Language to SQL
Peixian Ma, Boyan Li, Runzhi Jiang +3
Existing Natural Language to SQL (NL2SQL) solutions have made significant advancements, yet challenges persist in interpreting and translating NL queries, primarily due to users' l…