# False perspectives on human language: why statistics needs linguistics

arXiv: https://arxiv.org/abs/2302.08822 · HTML: https://mlsanity.com/paper/2302.08822/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 2 of 2)

- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [Open Sesame: Getting Inside BERT's Linguistic Knowledge](https://mlsanity.com/paper/1906.01698/) (2019)

## Built on by (most cited first, 2 of 2)

- [A Sentence is Worth a Thousand Pictures: Can Large Language Models Understand Hum4n L4ngu4ge and the W0rld behind W0rds?](https://mlsanity.com/paper/2308.00109/) (2023)
- [Large Language Models and Impossible Language Acquisition: "False Promise" or an Overturn of our Current Perspective towards AI](https://mlsanity.com/paper/2602.08437/) (2026)

## Abstract (arXiv)

A sharp tension exists about the nature of human language between two opposite parties: those who believe that statistical surface distributions, in particular using measures like surprisal, provide a better understanding of language processing, vs. those who believe that discrete hierarchical structures implementing linguistic information such as syntactic ones are a better tool. In this paper, we show that this dichotomy is a false one. Relying on the fact that statistical measures can be defined on the basis of either structural or non-structural models, we provide empirical evidence that only models of surprisal that reflect syntactic structure are able to account for language regularities.