# A Sentence is Worth a Thousand Pictures: Can Large Language Models Understand Hum4n L4ngu4ge and the W0rld behind W0rds? arXiv: https://arxiv.org/abs/2308.00109 ยท HTML: https://mlsanity.com/paper/2308.00109/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 17 of its 67 references that are in the map. - First posted on arXiv 26 Jul 2023. ## Builds on (most cited first, 17 of 17) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Theoretical Limitations of Self-Attention in Neural Sequence Models](https://mlsanity.com/paper/1906.06755/) (2019) - [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021) - [The Debate Over Understanding in AI's Large Language Models](https://mlsanity.com/paper/2210.13966/) (2022) - [A Systematic Investigation of Commonsense Knowledge in Large Language Models](https://mlsanity.com/paper/2111.00607/) (2021) - [Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators](https://mlsanity.com/paper/2307.05532/) (2023) - [Testing AI on language comprehension tasks reveals insensitivity to underlying meaning](https://mlsanity.com/paper/2302.12313/) (2023) - [On the proper role of linguistically-oriented deep net analysis in linguistic theorizing](https://mlsanity.com/paper/2106.08694/) (2021) - [Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale](https://mlsanity.com/paper/2203.00633/) (2022) - [Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review](https://mlsanity.com/paper/2202.12205/) (2022) - [DALL-E 2 Fails to Reliably Capture Common Syntactic Processes](https://mlsanity.com/paper/2210.12889/) (2022) - [ROSE: A Neurocomputational Architecture for Syntax](https://mlsanity.com/paper/2303.08877/) (2023) - [Natural Language Syntax Complies with the Free-Energy Principle](https://mlsanity.com/paper/2210.15098/) (2022) - [Large Language Models are not Models of Natural Language: they are Corpus Models](https://mlsanity.com/paper/2112.07055/) (2021) - [False perspectives on human language: why statistics needs linguistics](https://mlsanity.com/paper/2302.08822/) (2023) ## Built on by (most cited first, 17 of 17) - [How Linguistics Learned to Stop Worrying and Love the Language Models](https://mlsanity.com/paper/2501.17047/) (2025) - [Language in Vivo vs. in Silico: Size Matters but Larger Language Models Still Do Not Comprehend Language on a Par with Humans Due to Impenetrable Semantic Reference](https://mlsanity.com/paper/2404.14883/) (2024) - [Large Language Model probabilities cannot distinguish between possible and impossible language](https://mlsanity.com/paper/2509.15114/) (2025) - [Community size rather than grammatical complexity better predicts Large Language Model accuracy in a novel Wug Test](https://mlsanity.com/paper/2510.12463/) (2025) - [Enhancing multimodal analogical reasoning with Logic Augmented Generation](https://mlsanity.com/paper/2504.11190/) (2025) - [CV-Probes: Studying the interplay of lexical and world knowledge in visually grounded verb understanding](https://mlsanity.com/paper/2409.01389/) (2024) - [A Research Agenda for Usability and Generalisation in Reinforcement Learning](https://mlsanity.com/paper/2412.16970/) (2024) - [The Medical Metaphors Corpus (MCC)](https://mlsanity.com/paper/2508.07993/) (2025) - [Language models as tools for investigating the distinction between possible and impossible natural languages](https://mlsanity.com/paper/2512.09394/) (2025) - [Large language models have learned to use language](https://mlsanity.com/paper/2512.12447/) (2025) - [Large language models are not about natural language](https://mlsanity.com/paper/2512.13441/) (2025) - [Linguists should learn to love speech-based deep learning models](https://mlsanity.com/paper/2512.14506/) (2025) - [Are Language Models Models?](https://mlsanity.com/paper/2601.10421/) (2026) - [Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilities](https://mlsanity.com/paper/2604.09466/) (2026) - [You Can't Fight in Here! This is BBS!](https://mlsanity.com/paper/2604.09501/) (2026) - [Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear](https://mlsanity.com/paper/2605.10061/) (2026) - [Tracing the ongoing emergence of human-like reasoning in Large Language Models](https://mlsanity.com/paper/2605.21299/) (2026) ## Abstract (arXiv) Modern Artificial Intelligence applications show great potential for language-related tasks that rely on next-word prediction. The current generation of Large Language Models (LLMs) have been linked to claims about human-like linguistic performance and their applications are hailed both as a step towards artificial general intelligence and as a major advance in understanding the cognitive, and even neural basis of human language. To assess these claims, first we analyze the contribution of LLMs as theoretically informative representations of a target cognitive system vs. atheoretical mechanistic tools. Second, we evaluate the models' ability to see the bigger picture, through top-down feedback from higher levels of processing, which requires grounding in previous expectations and past world experience. We hypothesize that since models lack grounded cognition, they cannot take advantage of these features and instead solely rely on fixed associations between represented words and word vectors. To assess this, we designed and ran a novel 'leet task' (l33t t4sk), which requires decoding sentences in which letters are systematically replaced by numbers. The results suggest that humans excel in this task whereas models struggle, confirming our hypothesis. We interpret the results by identifying the key abilities that are still missing from the current state of development of these models, which require solutions that go beyond increased system scaling.