Large language models for software engineering: A systematic literature review

X Hou, Y Zhao, Y Liu, Z Yang, K Wang, L Li… - arXiv preprint arXiv …, 2023 - arxiv.org
Large Language Models (LLMs) have significantly impacted numerous domains, notably
including Software Engineering (SE). Nevertheless, a well-rounded understanding of the …

[HTML][HTML] A survey of GPT-3 family large language models including ChatGPT and GPT-4

KS Kalyan - Natural Language Processing Journal, 2023 - Elsevier
Large language models (LLMs) are a special class of pretrained language models (PLMs)
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …

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 …

LLM is Like a Box of Chocolates: the Non-determinism of ChatGPT in Code Generation

S Ouyang, JM Zhang, M Harman, M Wang - arXiv preprint arXiv …, 2023 - arxiv.org
There has been a recent explosion of research on Large Language Models (LLMs) for
software engineering tasks, in particular code generation. However, results from LLMs can …

Self-edit: Fault-aware code editor for code generation

K Zhang, Z Li, J Li, G Li, Z Jin - arXiv preprint arXiv:2305.04087, 2023 - arxiv.org
Large language models (LLMs) have demonstrated an impressive ability to generate codes
on competitive programming tasks. However, with limited sample numbers, LLMs still suffer …

Pangu-coder2: Boosting large language models for code with ranking feedback

B Shen, J Zhang, T Chen, D Zan, B Geng, A Fu… - arXiv preprint arXiv …, 2023 - arxiv.org
Large Language Models for Code (Code LLM) are flourishing. New and powerful models
are released on a weekly basis, demonstrating remarkable performance on the code …

[HTML][HTML] 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 …

Continual learning for large language models: A survey

T Wu, L Luo, YF Li, S Pan, TT Vu, G Haffari - arXiv preprint arXiv …, 2024 - arxiv.org
Large language models (LLMs) are not amenable to frequent re-training, due to high
training costs arising from their massive scale. However, updates are necessary to endow …

A survey of large language models for code: Evolution, benchmarking, and future trends

Z Zheng, K Ning, Y Wang, J Zhang, D Zheng… - arXiv preprint arXiv …, 2023 - arxiv.org
General large language models (LLMs), represented by ChatGPT, have demonstrated
significant potential in tasks such as code generation in software engineering. This has led …

Skcoder: A sketch-based approach for automatic code generation

J Li, Y Li, G Li, Z Jin, Y Hao, X Hu - 2023 IEEE/ACM 45th …, 2023 - ieeexplore.ieee.org
Recently, deep learning techniques have shown great success in automatic code
generation. Inspired by the code reuse, some researchers propose copy-based approaches …