The article discusses the potential of large language models (LLMs) in advancing neuroscience and biomedicine. LLMs, which have become powerful tools in natural language processing (NLP), are characterized by their ability to process vast amounts of text data, generate human-like text, and perform various tasks such as writing code, summarizing information, and playing games. The authors highlight several key advantages of LLMs: 1. **Enriching Neuroscience Datasets**: LLMs can add valuable meta-information to neuroscience datasets, such as advanced text sentiment analysis. 2. **Summarizing Information**: They can summarize large information sources, bridging divides between different neuroscience communities. 3. **Fusing Disparate Information**: LLMs enable the integration of diverse information sources relevant to the brain. 4. **Deconvolving Cognitive Concepts**: They help identify which cognitive concepts best grasp brain phenomena. The article also explores the scaling laws of LLMs, noting that larger models generally perform better with more training data and parameters. However, recent developments have shown that reducing model size can sometimes improve performance and reduce computational costs. The authors emphasize the importance of evaluation metrics in assessing LLM performance and the potential for LLMs to revolutionize transfer learning and few-shot learning. Additionally, the article discusses the application of LLMs to biological sequences, such as predicting the functional consequences of genetic variants and protein folding. It also highlights the use of LLMs for automated annotation in neuroscience, where they can enhance the accuracy and efficiency of manual annotation tasks. The authors suggest that LLMs can provide more stable and consistent annotations by capturing and manipulating subjective language, making them valuable tools for researchers in various fields.The article discusses the potential of large language models (LLMs) in advancing neuroscience and biomedicine. LLMs, which have become powerful tools in natural language processing (NLP), are characterized by their ability to process vast amounts of text data, generate human-like text, and perform various tasks such as writing code, summarizing information, and playing games. The authors highlight several key advantages of LLMs: 1. **Enriching Neuroscience Datasets**: LLMs can add valuable meta-information to neuroscience datasets, such as advanced text sentiment analysis. 2. **Summarizing Information**: They can summarize large information sources, bridging divides between different neuroscience communities. 3. **Fusing Disparate Information**: LLMs enable the integration of diverse information sources relevant to the brain. 4. **Deconvolving Cognitive Concepts**: They help identify which cognitive concepts best grasp brain phenomena. The article also explores the scaling laws of LLMs, noting that larger models generally perform better with more training data and parameters. However, recent developments have shown that reducing model size can sometimes improve performance and reduce computational costs. The authors emphasize the importance of evaluation metrics in assessing LLM performance and the potential for LLMs to revolutionize transfer learning and few-shot learning. Additionally, the article discusses the application of LLMs to biological sequences, such as predicting the functional consequences of genetic variants and protein folding. It also highlights the use of LLMs for automated annotation in neuroscience, where they can enhance the accuracy and efficiency of manual annotation tasks. The authors suggest that LLMs can provide more stable and consistent annotations by capturing and manipulating subjective language, making them valuable tools for researchers in various fields.