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@@ -7,7 +7,7 @@ In addition, biological events are context-dependent, for instance with respect
 
 Large Language Models (LLMs) of the current generation, in contrast, can access enormous amounts of knowledge, encoded (incomprehensibly) in their billions of parameters [@doi:10.48550/arxiv.2204.02311;@doi:10.48550/arxiv.2201.08239;@doi:10.48550/arxiv.2303.08774;@doi:10.1609/aaai.v36i11.21488].
 Trained correctly, they can recall and combine virtually limitless knowledge from their training set.
-ChatGPT has taken the world by storm, and many biomedical researchers already use LLMs in their daily work, for general as well as research tasks [@doi:10.1038/s41586-023-06792-0;@doi:10.1101/2023.04.16.537094;@doi:10.1038/s41587-023-01789-6].
+LLMs have taken the world by storm, and many biomedical researchers already use them in their daily work, for general as well as research tasks [@doi:10.1038/s41586-023-06792-0;@doi:10.1101/2023.04.16.537094;@doi:10.1038/s41587-023-01789-6].
 However, the current way of interacting with LLMs is predominantly manual, virtually non-reproducible, and their behaviour can be erratic.
 For instance, they are known to confabulate: they make up facts as they go along, and, to make matters worse, are convinced — and convincing — regarding the truth of their confabulations [@doi:10.1038/s41586-023-05881-4;@doi:10.1038/s41587-023-01789-6].
 While current efforts towards Artificial General Intelligence manage to ameliorate some of the shortcomings by ensembling multiple models [@{https://python.langchain.com}] with long-term memory stores [@{https://autogpt.net/}], the current generation of AI does not inspire adequate trust to be applied to biomedical problems without supervision [@doi:10.1038/s41586-023-05881-4].