  Do LLMs understand the meaning of the texts they generate? Do they possess a
semantic grounding? And how could we understand whether and what they
understand? I start the paper with the observation that we have recently
witnessed a generative turn in AI, since generative models, including LLMs, are
key for self-supervised learning. To assess the question of semantic grounding,
I distinguish and discuss five methodological ways. The most promising way is
to apply core assumptions of theories of meaning in philosophy of mind and
language to LLMs. Grounding proves to be a gradual affair with a
three-dimensional distinction between functional, social and causal grounding.
LLMs show basic evidence in all three dimensions. A strong argument is that
LLMs develop world models. Hence, LLMs are neither stochastic parrots nor
semantic zombies, but already understand the language they generate, at least
in an elementary sense.
