Natural language to code generation is an important application area of LLMs and has received wide attention from the community. The majority of relevant studies have …
Program synthesis has been long studied with recent approaches focused on directly using the power of Large Language Models (LLMs) to generate code. Programming benchmarks …
Abstract Language models of code (LMs) work well when the surrounding code provides sufficient context. This is not true when it becomes necessary to use types, functionality or …
Generative models of code, pretrained on large corpora of programs, have shown great success in translating natural language to code (Chen et al., 2021; Austin et al., 2021; Li et …
L Zhong, Z Wang - arXiv preprint arXiv:2308.10335, 2023 - arxiv.org
Recently, the large language models (LLMs) have shown extraordinary ability in understanding natural language and generating programming code. It has been a common …
Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations in …
Large language models (LLMs) have achieved impressive performance on code generation. However, for complex programming tasks, generating the correct solution in one go …
K Zhang, D Wang, J Xia… - Advances in Neural …, 2023 - proceedings.neurips.cc
Large language models (LLMs) excel at implementing code from functionality descriptions but struggle with algorithmic problems that require not only implementation but also …
Y Dong, J Ding, X Jiang, G Li, Z Li, Z Jin - arXiv preprint arXiv:2301.09043, 2023 - arxiv.org
A proper code evaluation metric (CEM) profoundly impacts the evolution of code generation, which is an important research field in NLP and software engineering. Prevailing match …