Natural language generation and understanding of big code for AI-assisted programming: A review

MF Wong, S Guo, CN Hang, SW Ho, CW Tan - Entropy, 2023‏ - mdpi.com
This paper provides a comprehensive review of the literature concerning the utilization of
Natural Language Processing (NLP) techniques, with a particular focus on transformer …

Applications of artificial intelligence in engineering and manufacturing: a systematic review

IK Nti, AF Adekoya, BA Weyori… - Journal of Intelligent …, 2022‏ - Springer
Engineering and manufacturing processes and systems designs involve many challenges,
such as dynamism, chaotic behaviours, and complexity. Of late, the arrival of big data, high …

The programmer's assistant: Conversational interaction with a large language model for software development

SI Ross, F Martinez, S Houde, M Muller… - Proceedings of the 28th …, 2023‏ - dl.acm.org
Large language models (LLMs) have recently been applied in software engineering to
perform tasks such as translating code between programming languages, generating code …

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

A Srivastava, A Rastogi, A Rao, AAM Shoeb… - arxiv preprint arxiv …, 2022‏ - arxiv.org
Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative impact, these new …

Palm: Scaling language modeling with pathways

A Chowdhery, S Narang, J Devlin, M Bosma… - Journal of Machine …, 2023‏ - jmlr.org
Large language models have been shown to achieve remarkable performance across a
variety of natural language tasks using few-shot learning, which drastically reduces the …

Retrieval-based prompt selection for code-related few-shot learning

N Nashid, M Sintaha, A Mesbah - 2023 IEEE/ACM 45th …, 2023‏ - ieeexplore.ieee.org
Large language models trained on massive code corpora can generalize to new tasks
without the need for task-specific fine-tuning. In few-shot learning, these models take as …

An empirical evaluation of GitHub copilot's code suggestions

N Nguyen, S Nadi - Proceedings of the 19th International Conference on …, 2022‏ - dl.acm.org
GitHub and OpenAI recently launched Copilot, an" AI pair programmer" that utilizes the
power of Natural Language Processing, Static Analysis, Code Synthesis, and Artificial …

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 …

Graph neural networks: foundation, frontiers and applications

L Wu, P Cui, J Pei, L Zhao, X Guo - … of the 28th ACM SIGKDD conference …, 2022‏ - dl.acm.org
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Program synthesis with large language models

J Austin, A Odena, M Nye, M Bosma… - arxiv preprint arxiv …, 2021‏ - arxiv.org
This paper explores the limits of the current generation of large language models for
program synthesis in general purpose programming languages. We evaluate a collection of …