23 citations · 124 across the 30 of their papers we have counts for
32 papers
ASTormer: An AST Structure-aware Transformer Decoder for Text-to-SQL
Ruisheng Cao, Hanchong Zhang, Hongshen Xu +4
Text-to-SQL aims to generate an executable SQL program given the user utterance and the corresponding database schema. To ensure the well-formedness of output SQLs, one prominent a…
ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought
Hanchong Zhang, Ruisheng Cao, Lu Chen +2
Recently Large Language Models (LLMs) have been proven to have strong abilities in various domains and tasks. We study the problem of prompt designing in the text-to-SQL task and a…
SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research
Liangtai Sun, Yang Han, Zihan Zhao +5
Recently, there has been growing interest in using Large Language Models (LLMs) for scientific research. Numerous benchmarks have been proposed to evaluate the ability of LLMs for…
Large Language Models Are Semi-Parametric Reinforcement Learning Agents
Danyang Zhang, Lu Chen, Situo Zhang +3
Inspired by the insights in cognitive science with respect to human memory and reasoning mechanism, a novel evolvable LLM-based (Large Language Model) agent framework is proposed a…
CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset
Hanchong Zhang, Jieyu Li, Lu Chen +5
The cross-domain text-to-SQL task aims to build a system that can parse user questions into SQL on complete unseen databases, and the single-domain text-to-SQL task evaluates the p…
On the Structural Generalization in Text-to-SQL
Jieyu Li, Lu Chen, Ruisheng Cao +5
Exploring the generalization of a text-to-SQL parser is essential for a system to automatically adapt the real-world databases. Previous works provided investigations focusing on l…