1.2k citations · 2.4k across the 18 of their papers we have counts for
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Benchmarking Language Models for Code Syntax Understanding
Da Shen, Xinyun Chen, Chenguang Wang +2
Pre-trained language models have demonstrated impressive performance in both natural language processing and program understanding, which represent the input as a token sequence wi…
Compositional Semantic Parsing with Large Language Models
Andrew Drozdov, Nathanael Schärli, Ekin Akyürek +5
Humans can reason compositionally when presented with new tasks. Previous research shows that appropriate prompting techniques enable large language models (LLMs) to solve artifici…
Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment
Yujian Gan, Xinyun Chen, Qiuping Huang +1
In text-to-SQL tasks -- as in much of NLP -- compositional generalization is a major challenge: neural networks struggle with compositional generalization where training and test d…
Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization
Yujian Gan, Xinyun Chen, Matthew Purver
Recently, there has been significant progress in studying neural networks for translating text descriptions into SQL queries under the zero-shot cross-domain setting. Despite achie…
Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
Yujian Gan, Xinyun Chen, Jinxia Xie +4
Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation. To bridge this gap, we propose an SQ…
Towards Robustness of Text-to-SQL Models against Synonym Substitution
Yujian Gan, Xinyun Chen, Qiuping Huang +4
Recently, there has been significant progress in studying neural networks to translate text descriptions into SQL queries. Despite achieving good performance on some public benchma…