3 citations · 5 across the 6 of their papers we have counts for
6 papers
Data Agents: Agentic Data Systems
Guoliang Li, Peiyao Zhou, Xuanhe Zhou +3
Traditional data systems face profound limitations in the AI era, relying on human-crafted pipelines, lacking semantic understanding of heterogeneous data, and operating through ri…
Data Agents: Levels, State of the Art, and Open Problems
Yuyu Luo, Guoliang Li, Ju Fan +1
Data agents are an emerging paradigm that leverages large language models (LLMs) and tool-using agents to automate data management, preparation, and analysis tasks. However, the te…
A Survey of Data Agents: Emerging Paradigm or Overstated Hype?
Yizhang Zhu, Liangwei Wang, Chenyu Yang +22
The rapid advancement of large language models (LLMs) has spurred the emergence of data agents, autonomous systems designed to orchestrate Data + AI ecosystems for tackling complex…
A Unified Model for Cardinality Estimation by Learning from Data and Queries via Sum-Product Networks
Jiawei Liu, Ju Fan, Tongyu Liu +5
Cardinality estimation is a fundamental component in database systems, crucial for generating efficient execution plans. Despite advancements in learning-based cardinality estimati…
Alpha-SQL: Zero-Shot Text-to-SQL using Monte Carlo Tree Search
Boyan Li, Jiayi Zhang, Ju Fan +4
Text-to-SQL, which enables natural language interaction with databases, serves as a pivotal method across diverse industries. With new, more powerful large language models (LLMs) e…
Harnessing Diversity for Important Data Selection in Pretraining Large Language Models
Chi Zhang, Huaping Zhong, Kuan Zhang +10
Data selection is of great significance in pre-training large language models, given the variation in quality within the large-scale available training corpora. To achieve this, re…