Large language models (LLMs) have improved document information extraction, but turning extracted facts into relational databases remains fundamentally difficult. The challenge is that extraction is local to text, whereas database construction must satisfy global semantics defined by schemas, keys, and integrity cons...
Zhengxuan Zhang, Zhuo-Wen Liang, Hai-Xun Wang et al.· Proceedings of the VLDB Endo...· 0 citations
Doc2DB-Bench is introduced, a benchmark for Document-to-Database construction, containing 203 long-document instances across 42 schemas and seven domain groups, with 117 entity tables, 132 relationship tables, 7,341 rows, and 41,935 cells, which provides a testbed for reliable, auditable, and relationally faithful LLM-...
Zhuo-Wen Liang, Zhengxuan Zhang, Jia-Yang Wang et al.· 1 citation
DataSpace, a benchmark in which data agents produce verifiable tabular results from task-local heterogeneous workspaces, is introduced and key challenges for improving data-agent reliability are identified.
Boyan Li, Zhuo-Wen Liang, Yu-Peng Xie et al.· 1 citation
Experiments on public benchmarks and real-world industrial BI workloads show that QwenPaw-Data improves both verifiable data access capability and higher-level analytical quality, offering a practical foundation for reliable, traceable, and continuously improving enterprise data agents.
