# Meaning and understanding in large language models

arXiv: https://arxiv.org/abs/2310.17407 · HTML: https://mlsanity.com/paper/2310.17407/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Experience Grounds Language](https://mlsanity.com/paper/2004.10151/) (2020)
- [Meaning without reference in large language models](https://mlsanity.com/paper/2208.02957/) (2022)
- [Word meaning in minds and machines](https://mlsanity.com/paper/2008.01766/) (2020)
- [The Vector Grounding Problem](https://mlsanity.com/paper/2304.01481/) (2023)
- [Do Language Models' Words Refer?](https://mlsanity.com/paper/2308.05576/) (2023)
- [The Singleton Fallacy: Why Current Critiques of Language Models Miss the Point](https://mlsanity.com/paper/2102.04310/) (2021)
- [Making AI meaningful again](https://mlsanity.com/paper/1901.02918/) (2019)
- [There is no Artificial General Intelligence](https://mlsanity.com/paper/1906.05833/) (2019)

## Built on by (most cited first, 5 of 5)

- [Efficient Emotion-Aware Iconic Gesture Prediction for Robot Co-Speech](https://mlsanity.com/paper/2604.11417/) (2026)
- [Do Large Language Models Advocate for Inferentialism?](https://mlsanity.com/paper/2412.14501/) (2024)
- [Grandes modelos de lenguaje: de la predicci'on de palabras a la comprensi'on?](https://mlsanity.com/paper/2502.18205/) (2025)
- [Pragmatics beyond humans: meaning, communication, and LLMs](https://mlsanity.com/paper/2508.06167/) (2025)
- [Grounding Without Corrective Control: Truth-Tracking Profiles for Large Language Models](https://mlsanity.com/paper/2608.14252/) (2026)

## Abstract (arXiv)

Can a machine understand the meanings of natural language? Recent developments in the generative large language models (LLMs) of artificial intelligence have led to the belief that traditional philosophical assumptions about machine understanding of language need to be revised. This article critically evaluates the prevailing tendency to regard machine language performance as mere syntactic manipulation and the simulation of understanding, which is only partial and very shallow, without sufficient referential grounding in the world. The aim is to highlight the conditions crucial to attributing natural language understanding to state-of-the-art LLMs, where it can be legitimately argued that LLMs not only use syntax but also semantics, their understanding not being simulated but duplicated; and determine how they ground the meanings of linguistic expressions.