Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada; TheNeuro - Montreal Neurological Institute (MNI), Department of Biomedical Engineering, McGill University, Montreal, QC, Canada.
Mindstate Design Labs, San Francisco, CA, USA.
Neuron. 2024 Mar 6;112(5):698-717. doi: 10.1016/j.neuron.2024.01.016. Epub 2024 Feb 9.
Large language models (LLMs) are a new asset class in the machine-learning landscape. Here we offer a primer on defining properties of these modeling techniques. We then reflect on new modes of investigation in which LLMs can be used to reframe classic neuroscience questions to deliver fresh answers. We reason that LLMs have the potential to (1) enrich neuroscience datasets by adding valuable meta-information, such as advanced text sentiment, (2) summarize vast information sources to overcome divides between siloed neuroscience communities, (3) enable previously unthinkable fusion of disparate information sources relevant to the brain, (4) help deconvolve which cognitive concepts most usefully grasp phenomena in the brain, and much more.
大型语言模型(LLMs)是机器学习领域的一种新型资产类别。在这里,我们提供了一个入门读物,介绍了这些建模技术的定义属性。然后,我们反思了可以使用 LLM 重新构建经典神经科学问题以提供新答案的新研究模式。我们认为,LLM 具有以下潜力:(1)通过添加有价值的元信息(如高级文本情绪)来丰富神经科学数据集;(2)总结大量信息源,以克服神经科学社区之间的隔阂;(3)实现以前无法想象的不同信息源的融合,这些信息源与大脑相关;(4)帮助推断哪些认知概念最有助于理解大脑中的现象,等等。