2 citations · 2 across the 3 of their papers we have counts for
6 papers
CODA-BENCH: Can Code Agents Handle Data-Intensive Tasks?
Yuxin Zhang, Ju Fan, Meihao Fan +2
Advanced agents are increasingly demonstrating the potential to operate as autonomous engineers, creating a growing demand for evaluation benchmarks that capture the complexity of…
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…
DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
Shaolei Zhang, Ju Fan, Meihao Fan +2
Autonomous data science, from raw data sources to analyst-grade deep research reports, has been a long-standing challenge, and is now becoming feasible with the emergence of powerf…
AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework
Meihao Fan, Ju Fan, Nan Tang +3
Answering natural language (NL) questions about tables, known as Tabular Question Answering (TQA), is crucial because it allows users to quickly and efficiently extract meaningful…