Large Language Models (LLMs) have significant limitations in modeling reality, primarily due to their lack of understanding of the physical world and their inability to process sensory data effectively. According to the excerpts, LLMs lack essential characteristics like persistent memory, reasoning, and planning capabilities, which are vital for intelligent systems 00:10:00.

LLMs do not possess an intrinsic understanding of the physical world because they aren't trained with visual data or video, which limits their ability to understand intuitive physics and common sense reasoning about physical reality 00:18:30. Language is merely an approximate representation of reality, and much richer environments exist beyond what language can express 00:14:00.

To overcome these shortcomings, incorporating different sensory data, such as visual and audio representations, in training LLMs can help them make better decisions and understand the world more comprehensively. This approach allows LLMs to process visual representations of images, videos, or audio, although such integrations are currently considered 'hacks' and are not yet fully integrated into LLMs 00:17:00 and 00:18:00.

Overall, grounding intelligence in reality through sensory data is essential, as language alone is insufficient for constructing a comprehensive world model 00:13:30.