How to write effective prompts for large language models
Bibliographic Data
| ID | 4615686 |
|---|---|
| Authors | Zhicheng Lin (0000-0002-6864-6559, University of Science and Technology of China, corresponding author) |
| Year | 2024 |
| Volume | 8 |
| Issue | 4 |
| Pages | 611-615 |
| Publication date | 2024-03-04 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Nature Human Behaviour (JOURNAL) |
| Journal identifiers | ISSN: 2397-3374 • E-ISSN: 2397-3374 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1038/s41562-024-01847-2 |
| PMID | 38438650 |
| OpenAlex | W4392376454 |
| Language | EN |
| Citations received | 10 |
| References cited | 8 |
Cognitive science · Data science · Human–computer interaction · Language model · Natural language processing · Artificial Intelligence in Healthcare and Education · Computer Science · Explainable Artificial Intelligence (XAI · Psychology · Topic Modeling · Artificial Intelligence
The Art of Creative Inquiry—From Question Asking to Prompt Engineering
Large Language Models as Psychological Simulators
Generating Experimental Text Stimuli for Psychological Research Using ChatGPT
Using large language models to facilitate academic work in the psychological sciences
Effectiveness and guidance of artificial intelligence chatbots in diagnosing exponential expression errors
Comparative analysis of GPT-4, Gemini, and Ernie as gloss sign language translators in special education
Examining language learners’ GenAI-assisted writing self-efficacy profiles and the relationship with their writing self-regulated learning strategies
GISedu-GPT
TMChatGPT for automated writing evaluation
Machine Bias. How Do Generative Language Models Answer Opinion Polls
| Unique citing works | 10 |
|---|---|
| Citations per year | 5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 9 |