# On the Compatibility of Generative AI and Generative Linguistics

arXiv: https://arxiv.org/abs/2411.10533 · HTML: https://mlsanity.com/paper/2411.10533/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 91 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Neural Networks and the Chomsky Hierarchy](https://mlsanity.com/paper/2207.02098/) (2022)
- [Compound Probabilistic Context-Free Grammars for Grammar Induction](https://mlsanity.com/paper/1906.10225/) (2019)
- [Language models are not naysayers: An analysis of language models on negation benchmarks](https://mlsanity.com/paper/2306.08189/) (2023)
- [Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders](https://mlsanity.com/paper/1904.02142/) (2019)
- [Visually Grounded Neural Syntax Acquisition](https://mlsanity.com/paper/1906.02890/) (2019)
- [On the proper role of linguistically-oriented deep net analysis in linguistic theorizing](https://mlsanity.com/paper/2106.08694/) (2021)
- [Visually Grounded Compound PCFGs](https://mlsanity.com/paper/2009.12404/) (2020)
- [Minimum Description Length Recurrent Neural Networks](https://mlsanity.com/paper/2111.00600/) (2021)
- [Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding](https://mlsanity.com/paper/2310.19671/) (2023)
- [The Return of Lexical Dependencies: Neural Lexicalized PCFGs](https://mlsanity.com/paper/2007.15135/) (2020)
- [Mathematical Structure of Syntactic Merge](https://mlsanity.com/paper/2305.18278/) (2023)
- [Why Linguistics Will Thrive in the 21st Century: A Reply to Piantadosi (2023)](https://mlsanity.com/paper/2308.03228/) (2023)
- [A case for deep learning in semantics](https://mlsanity.com/paper/1809.03068/) (2018)
- [Reframing linguistic bootstrapping as joint inference using visually-grounded grammar induction models](https://mlsanity.com/paper/2406.11977/) (2024)

## Abstract (arXiv)

In mid-20th century, the linguist Noam Chomsky established generative linguistics, and made significant contributions to linguistics, computer science, and cognitive science by developing the computational and philosophical foundations for a theory that defined language as a formal system, instantiated in human minds or artificial machines. These developments in turn ushered a wave of research on symbolic Artificial Intelligence (AI). More recently, a new wave of non-symbolic AI has emerged with neural Language Models (LMs) that exhibit impressive linguistic performance, leading many to question the older approach and wonder about the the compatibility of generative AI and generative linguistics. In this paper, we argue that generative AI is compatible with generative linguistics and reinforces its basic tenets in at least three ways. First, we argue that LMs are formal generative models as intended originally in Chomsky's work on formal language theory. Second, LMs can help develop a program for discovery procedures as defined by Chomsky's "Syntactic Structures". Third, LMs can be a major asset for Chomsky's minimalist approach to Universal Grammar and language acquisition. In turn, generative linguistics can provide the foundation for evaluating and improving LMs as well as other generative computational models of language.