6 papers
Towards Probabilistic Question Answering Over Tabular Data
Chen Shen, Sajjadur Rahman, Estevam Hruschka
Current approaches for question answering (QA) over tabular data, such as NL2SQL systems, perform well for factual questions where answers are directly retrieved from tables. Howev…
Effectiveness of Prompt Optimization in NL2SQL Systems
Sairam Gurajada, Eser Kandogan, Sajjadur Rahman
NL2SQL approaches have greatly benefited from the impressive capabilities of large language models (LLMs). In particular, bootstrapping an NL2SQL system for a specific domain can b…
CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era
Yanlin Feng, Simone Papicchio, Sajjadur Rahman
Retrieval from graph data is crucial for augmenting large language models (LLM) with both open-domain knowledge and private enterprise data, and it is also a key component in the r…
LakeVisage: Towards Scalable, Flexible and Interactive Visualization Recommendation for Data Discovery over Data Lakes
Yihao Hu, Jin Wang, Sajjadur Rahman
Data discovery from data lakes is an essential application in modern data science. While many previous studies focused on improving the efficiency and effectiveness of data discove…
Towards Operationalizing Heterogeneous Data Discovery
Jin Wang, Yanlin Feng, Chen Shen +2
Querying and exploring massive collections of data sources, such as data lakes, has been an essential research topic in the database community. Although many efforts have been paid…
MageSQL: Enhancing In-context Learning for Text-to-SQL Applications with Large Language Models
Chen Shen, Jin Wang, Sajjadur Rahman +1
The text-to-SQL problem aims to translate natural language questions into SQL statements to ease the interaction between database systems and end users. Recently, Large Language Mo…