The advent of large language models trained on code (code LLMs) has led to significant
progress in language-to-code generation. State-of-the-art approaches in this area combine …

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 …

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 …

Large language models (LLMs) excel at implementing code from functionality descriptions
but struggle with algorithmic problems that require not only implementation but also …

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 …