# "Understanding AI": Semantic Grounding in Large Language Models

arXiv: https://arxiv.org/abs/2402.10992 · HTML: https://mlsanity.com/paper/2402.10992/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debates](https://mlsanity.com/paper/2401.03910/) (2024)

## Built on by (most cited first, 7 of 7)

- [Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches](https://mlsanity.com/paper/2501.03151/) (2025)
- [Probing the Preferences of a Language Model: Integrating Verbal and Behavioral Tests of AI Welfare](https://mlsanity.com/paper/2509.07961/) (2025)
- [Think Deep, Speak Once: Relit, A Recursive Latent Implicit Transformer Framework](https://mlsanity.com/paper/2608.08113/) (2026)
- [NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classification](https://mlsanity.com/paper/2402.16910/) (2024)
- [Quantification and object perception in Multimodal Large Language Models and human linguistic cognition](https://mlsanity.com/paper/2511.08126/) (2025)
- [Neuroscience-Inspired Analyses of Visual Interestingness in Multimodal Transformers](https://mlsanity.com/paper/2605.08188/) (2026)
- [Words as Difference Makers: How Large Language Models Determine Causal Structure in Text](https://mlsanity.com/paper/2606.22430/) (2026)

## Abstract (arXiv)

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.