# Generative linguistics contribution to artificial intelligence: Where this contribution lies?

arXiv: https://arxiv.org/abs/2410.20221 · HTML: https://mlsanity.com/paper/2410.20221/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 101 references that are in the map.
- First posted on arXiv 26 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies](https://mlsanity.com/paper/1611.01368/) (2016)
- [What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models](https://mlsanity.com/paper/1907.13528/) (2019)
- [Colorless green recurrent networks dream hierarchically](https://mlsanity.com/paper/1803.11138/) (2018)
- [Implicit Representations of Meaning in Neural Language Models](https://mlsanity.com/paper/2106.00737/) (2021)
- [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://mlsanity.com/paper/2301.08745/) (2023)
- [Syntactic Structure from Deep Learning](https://mlsanity.com/paper/2004.10827/) (2020)
- [On The Evaluation of Machine Translation Systems Trained With Back-Translation](https://mlsanity.com/paper/1908.05204/) (2019)
- [Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition](https://mlsanity.com/paper/1902.10618/) (2019)
- [Assessing Composition in Sentence Vector Representations](https://mlsanity.com/paper/1809.03992/) (2018)
- [Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization](https://mlsanity.com/paper/2011.02417/) (2020)
- [Syntax-semantics interface: an algebraic model](https://mlsanity.com/paper/2311.06189/) (2023)

## Built on by (most cited first, 8 of 8)

- [Artificial intelligence contribution to translation industry: looking back and forward](https://mlsanity.com/paper/2411.19855/) (2024)
- [Machine learning and emoji prediction: How much accuracy can MARBERT achieve?](https://mlsanity.com/paper/2604.21108/) (2026)
- [Non-native speakers of English or ChatGPT: Who thinks better?](https://mlsanity.com/paper/2412.00457/) (2024)
- [Revisiting the syntax of imperatives in Yemeni Arabic: An Agree across phases approach](https://mlsanity.com/paper/2605.08447/) (2026)
- [The Syntax of qulk-clauses in Yemeni Ibbi Arabic: A Minimalist Approach](https://mlsanity.com/paper/2512.22376/) (2025)
- [Current LLMs still cannot 'talk much' about grammar modules: Evidence from syntax](https://mlsanity.com/paper/2603.20114/) (2026)
- [The syntax of wh-agreement in Yemeni Ibbi Arabic](https://mlsanity.com/paper/2607.04986/) (2026)
- [A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs](https://mlsanity.com/paper/2610.02529/) (2026)

## Abstract (arXiv)

This article aims to characterize Generative linguistics (GL) contribution to artificial intelligence (AI), alluding to the debate among linguists and AI scientists on whether linguistics belongs to humanities or science. In this article, I will try not to be biased as a linguist, studying the phenomenon from an independent scientific perspective. The article walks the researcher/reader through the scientific theorems and rationales involved in AI which belong from GL, specifically the Chomsky School. It, thus, provides good evidence from syntax, semantics, language faculty, Universal Grammar, computational system of human language, language acquisition, human brain, programming languages (e.g. Python), Large Language Models, and unbiased AI scientists that this contribution is huge, and that this contribution cannot be denied. It concludes that however the huge GL contribution to AI, there are still points of divergence including the nature and type of language input.