2 citations · 2 across the 4 of their papers we have counts for
5 papers
Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQL
Qifeng Cai, Hao Liang, Chang Xu +3
The data-centric paradigm has emerged as a pivotal direction in artificial intelligence (AI), emphasizing the role of high-quality training data. This shift is especially critical…
The First Prompt Counts the Most! An Evaluation of Large Language Models on Iterative Example-Based Code Generation
Yingjie Fu, Bozhou Li, Linyi Li +2
The capabilities of Large Language Models (LLMs) in code generation have been extensively studied, particularly for implementing target functionalities from natural-language descri…
InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models
Linyi Li, Shijie Geng, Zhenwen Li +7
Large Language Models for code (code LLMs) have witnessed tremendous progress in recent years. With the rapid development of code LLMs, many popular evaluation benchmarks, such as…
Using LLM to select the right SQL Query from candidates
Zhenwen Li, Tao Xie
Text-to-SQL models can generate a list of candidate SQL queries, and the best query is often in the candidate list, but not at the top of the list. An effective re-rank method can…
Data Transformation to Construct a Dataset for Generating Entity-Relationship Model from Natural Language
Zhenwen Li, Jian-Guang Lou, Tao Xie
In order to reduce the manual cost of designing ER models, recent approaches have been proposed to address the task of NL2ERM, i.e., automatically generating entity-relationship (E…