Ten simple rules for optimal and careful use of generative AI in science

作者：Mohamed Helmy, Lingling Jin, Amr Alhossary, Tamer Mansour, Diogo Pellegrina, Kumar Selvarajoo · 发表于：PLoS Computational Biology · 年份：2025 · DOI：10.1371/journal.pcbi.1013588 · 被引用次数：10 · 研究领域：Artificial Intelligence in Healthcare and Education、Quantum many-body systems、Scientific Computing and Data Management

Modern AI technologies leverage natural language processing (NLP), a subfield of AI dedicated to understanding, interpreting, and generating human language for developing large language models (LLMs), which have significantly advanced the capabilities of AI systems. These models can perform complex language tasks such as text generation, summarization, translation, and sentiment analysis, with unprecedented accuracy. The two main kinds of pre-training LLMs are the BERT-like models (e.g., BioBERT, proteinBERT, and PubMedBERT used primarily for language understanding; and the GPT-like models (e.g., BioGPT and ChatGPT-4o) used primarily for language generation