Comparing Large Language Models and Human Programmers for Generating Programming Code
Abstract The performance of seven large language models (LLMs) in generating programming code using various prompt strategies, programming languages, and task difficulties is systematically evaluated. GPT‐4 substantially outperforms other LLMs, including Gemini Ultra and Claude 2. The coding perform...
Saved in:
| Main Authors: | Wenpin Hou, Zhicheng Ji |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-02-01
|
| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202412279 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Characteristics and perceived suitability of artificial intelligence-driven sports coaches: a pilot study on psychological and perceptual factors
by: Carlo Dindorf, et al.
Published: (2025-05-01) -
Large language models and the future of gastroenterology: dissecting the biopolitics of data in a global health ecosystem
by: Bilal Irfan, et al.
Published: (2025-08-01) -
Radiology-GPT: A large language model for radiology
by: Zhengliang Liu, et al.
Published: (2025-06-01) -
Are Large Language Models Intelligent? Are Humans?
by: Olle Häggström
Published: (2023-08-01) -
NeuralConstraints: integrating a neural generative model with constraint-based composition
by: Juan S. Vassallo, et al.
Published: (2025-04-01)