24 citations · 50 across the 8 of their papers we have counts for
5 papers · 1 filter
CodeExp: Explanatory Code Document Generation
Haotian Cui, Chenglong Wang, Junjie Huang +5
Developing models that can automatically generate detailed code explanation can greatly benefit software maintenance and programming education. However, existing code-to-text gener…
Execution-based Evaluation for Data Science Code Generation Models
Junjie Huang, Chenglong Wang, Jipeng Zhang +6
Code generation models can benefit data scientists' productivity by automatically generating code from context and text descriptions. An important measure of the modeling progress…
Interactive Code Generation via Test-Driven User-Intent Formalization
Shuvendu K. Lahiri, Sarah Fakhoury, Aaditya Naik +8
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, w…
Fault-Aware Neural Code Rankers
Jeevana Priya Inala, Chenglong Wang, Mei Yang +5
Large language models (LLMs) have demonstrated an impressive ability to generate code for various programming tasks. In many instances, LLMs can generate a correct program for a ta…
Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions
Ansong Ni, Jeevana Priya Inala, Chenglong Wang +4
Pretrained language models have shown superior performance on many natural language processing tasks, yet they still struggle at multi-step formal reasoning tasks like grade school…