Finetuning large language models (LLMs) on instructions leads to vast performance
improvements on natural language tasks. We apply instruction tuning using code …

Code-generating large language models map natural language to code. However, only a
small portion of the infinite space of naturalistic utterances is effective at guiding code …

Large language models have demonstrated the ability to generate both natural language
and programming language text. Although contemporary code generation models are …

The task of generating code from a natural language description, or NL2Code, is considered
a pressing and significant challenge in code intelligence. Thanks to the rapid development …

We present new benchmarks on evaluation code generation models: MBXP and Multilingual
HumanEval, and MathQA-X. These datasets cover over 10 programming languages and are …

Code Generation aims at generating relevant code fragments according to given natural
language descriptions. In the process of software development, there exist a large number of …

To extend the scope of coding queries to more realistic settings, we propose ODEX, the first
Open-Domain EXecution-based natural language (NL) to Python code generation dataset …

Recently, pre-trained large language models (LLMs) have shown impressive abilities in
generating codes from natural language descriptions, repairing buggy codes, translating …

Software engineers working with the same programming language (PL) may speak different
natural languages (NLs) and vice versa, erecting huge barriers to communication and …

Multilingual Large Language Models are capable of using powerful Large Language
Models to handle and respond to queries in multiple languages, which achieves remarkable …