activity
20232026
most citedOmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale

2 citations · 7 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models

Xinmei Huang, Jie Song, Peng Li +10

Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines. Existing S…

cs.DB2026

IDSTune: A Multi-Agent Collaborative Framework for Integrated Database System Tuning

Yiyan Li, Guanli Liu, Renata Borovica-Gajic +6

Database tuning is critical for achieving high performance in modern database management systems (DBMSs). Existing methods typically optimize a single component---knobs, indexes, o…

cs.DB2025

LLMIA: An Out-of-the-Box Index Advisor via In-Context Learning with LLMs

Xinxin Zhao, Xinmei Huang, Haoyang Li +7

Index recommendation is crucial for optimizing database performance. However, existing heuristic- and learning-based methods often rely on inefficient exhaustive search and estimat…

cs.CL2025★ 2 cited

OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale

Haoyang Li, Shang Wu, Xiaokang Zhang +9

Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements…

cs.AI2024★ 1 cited

E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model

Xinmei Huang, Haoyang Li, Jing Zhang +7

Database knob tuning is a significant challenge for database administrators, as it involves tuning a large number of configuration knobs with continuous or discrete values to achie…

cs.CL2023★ 2 cited

Chain of Thought Prompting Elicits Knowledge Augmentation

Dingjun Wu, Jing Zhang, Xinmei Huang

The knowledge-augmented deep learning paradigm refers to a paradigm in which domain knowledge is identified and integrated into deep models. Conventional methods typically employ t…

cs.AI2023★ 2 cited

FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering

Lingxi Zhang, Jing Zhang, Yanling Wang +5

The generalization problem on KBQA has drawn considerable attention. Existing research suffers from the generalization issue brought by the entanglement in the coarse-grained model…