most citedCodeT: Code Generation with Generated Tests

65 citations · 86 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2023

How Do In-Context Examples Affect Compositional Generalization?

Shengnan An, Zeqi Lin, Qiang Fu +4

Compositional generalization--understanding unseen combinations of seen primitives--is an essential reasoning capability in human intelligence. The AI community mainly studies this…

cs.CL2023

Uncovering and Categorizing Social Biases in Text-to-SQL

Yan Liu, Yan Gao, Zhe Su +3

Content Warning: This work contains examples that potentially implicate stereotypes, associations, and other harms that could be offensive to individuals in certain social groups.}…

cs.CL20234 cited

Uncovering and Quantifying Social Biases in Code Generation

Yan Liu, Xiaokang Chen, Yan Gao +6

With the popularity of automatic code generation tools, such as Copilot, the study of the potential hazards of these tools is gaining importance. In this work, we explore the socia…

cs.CL2023

TACR: A Table-alignment-based Cell-selection and Reasoning Model for Hybrid Question-Answering

Jian Wu, Yicheng Xu, Yan Gao +3

Hybrid Question-Answering (HQA), which targets reasoning over tables and passages linked from table cells, has witnessed significant research in recent years. A common challenge in…

cs.SE202314 cited

Explainable Automated Debugging via Large Language Model-driven Scientific Debugging

Sungmin Kang, Bei Chen, Shin Yoo +1

Automated debugging techniques have the potential to reduce developer effort in debugging, and have matured enough to be adopted by industry. However, one critical issue with exist…

cs.LG20233 cited

Does Deep Learning Learn to Abstract? A Systematic Probing Framework

Shengnan An, Zeqi Lin, Bei Chen +3

Abstraction is a desirable capability for deep learning models, which means to induce abstract concepts from concrete instances and flexibly apply them beyond the learning context.…