Octopack: Instruction tuning code large language models

N Muennighoff, Q Liu, A Zebaze, Q Zheng… - … 2023 Workshop on …, 2023‏ - openreview.net
Finetuning large language models (LLMs) on instructions leads to vast performance
improvements on natural language tasks. We apply instruction tuning using code …

MultiPL-E: a scalable and polyglot approach to benchmarking neural code generation

F Cassano, J Gouwar, D Nguyen… - IEEE Transactions …, 2023‏ - ieeexplore.ieee.org
Large language models have demonstrated the ability to generate both natural language
and programming language text. Although contemporary code generation models are …

Large language models meet nl2code: A survey

D Zan, B Chen, F Zhang, D Lu, B Wu, B Guan… - arxiv preprint arxiv …, 2022‏ - arxiv.org
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 …

Generative software engineering

Y Huang, Y Chen, X Chen, J Chen, R Peng… - arxiv preprint arxiv …, 2024‏ - arxiv.org
The rapid development of deep learning techniques, improved computational power, and
the availability of vast training data have led to significant advancements in pre-trained …

“What it wants me to say”: Bridging the abstraction gap between end-user programmers and code-generating large language models

MX Liu, A Sarkar, C Negreanu, B Zorn… - Proceedings of the …, 2023‏ - dl.acm.org
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 …

Multilingual large language model: A survey of resources, taxonomy and frontiers

L Qin, Q Chen, Y Zhou, Z Chen, Y Li, L Liao… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Multilingual Large Language Models are capable of using powerful Large Language
Models to handle and respond to queries in multiple languages, which achieves remarkable …

Multi-lingual evaluation of code generation models

B Athiwaratkun, SK Gouda, Z Wang, X Li, Y Tian… - arxiv preprint arxiv …, 2022‏ - arxiv.org
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 …

Unifying the perspectives of nlp and software engineering: A survey on language models for code

Z Zhang, C Chen, B Liu, C Liao, Z Gong, H Yu… - arxiv preprint arxiv …, 2023‏ - arxiv.org
In this work we systematically review the recent advancements in software engineering with
language models, covering 70+ models, 40+ evaluation tasks, 180+ datasets, and 900 …

Execution-based evaluation for open-domain code generation

Z Wang, S Zhou, D Fried, G Neubig - arxiv preprint arxiv:2212.10481, 2022‏ - arxiv.org
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 …

A survey of neural code intelligence: Paradigms, advances and beyond

Q Sun, Z Chen, F Xu, K Cheng, C Ma, Z Yin… - arxiv preprint arxiv …, 2024‏ - arxiv.org
Neural Code Intelligence--leveraging deep learning to understand, generate, and optimize
code--holds immense potential for transformative impacts on the whole society. Bridging the …