"Understanding AI": Semantic Grounding in Large Language Models

Understand

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.