2 citations · 2 across the 2 of their papers we have counts for
4 papers
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…
Reward-SQL: Boosting Text-to-SQL via Stepwise Execution-Aware Reasoning and Process-Supervised Rewards
Yuxin Zhang, Meihao Fan, Ju Fan +5
Recent advances in large language models (LLMs) trained with reinforcement learning (RL) have improved Text-to-SQL performance. However, RL-based approaches still struggle with com…
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…
A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?
Xinyu Liu, Shuyu Shen, Boyan Li +7
Translating users' natural language queries (NL) into SQL queries (i.e., Text-to-SQL, a.k.a. NL2SQL) can significantly reduce barriers to accessing relational databases and support…