# Why Linguistics Will Thrive in the 21st Century: A Reply to Piantadosi (2023) arXiv: https://arxiv.org/abs/2308.03228 ยท HTML: https://mlsanity.com/paper/2308.03228/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 128 references that are in the map. - First posted on arXiv 6 Aug 2023. ## Builds on (most cited first, 30 of 32) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020) - [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019) - [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018) - [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://mlsanity.com/paper/1902.01007/) (2019) - [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020) - [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019) - [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019) - [Linguistic Knowledge and Transferability of Contextual Representations](https://mlsanity.com/paper/1903.08855/) (2019) - [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019) - [Analysis Methods in Neural Language Processing: A Survey](https://mlsanity.com/paper/1812.08951/) (2018) - [Dissecting Contextual Word Embeddings: Architecture and Representation](https://mlsanity.com/paper/1808.08949/) (2018) - [Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State](https://mlsanity.com/paper/1903.03260/) (2019) - [When Do You Need Billions of Words of Pretraining Data?](https://mlsanity.com/paper/2011.04946/) (2020) - [Syntactic Structure from Deep Learning](https://mlsanity.com/paper/2004.10827/) (2020) - [What Artificial Neural Networks Can Tell Us About Human Language Acquisition](https://mlsanity.com/paper/2208.07998/) (2022) - [Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models](https://mlsanity.com/paper/2110.07736/) (2021) - [Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators](https://mlsanity.com/paper/2307.05532/) (2023) - [On the proper role of linguistically-oriented deep net analysis in linguistic theorizing](https://mlsanity.com/paper/2106.08694/) (2021) - [Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT](https://mlsanity.com/paper/2101.11043/) (2021) - [What if This Modified That? Syntactic Interventions via Counterfactual Embeddings](https://mlsanity.com/paper/2105.14002/) (2021) - [Artificial neural networks for neuroscientists: A primer](https://mlsanity.com/paper/2006.01001/) (2020) - [What they do when in doubt: a study of inductive biases in seq2seq learners](https://mlsanity.com/paper/2006.14953/) (2020) - [GAIT-prop: A biologically plausible learning rule derived from backpropagation of error](https://mlsanity.com/paper/2006.06438/) (2020) - [Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection](https://mlsanity.com/paper/1906.01280/) (2019) - [Inflecting when there's no majority: Limitations of encoder-decoder neural networks as cognitive models for German plurals](https://mlsanity.com/paper/2005.08826/) (2020) - [Kickback cuts Backprop's red-tape: Biologically plausible credit assignment in neural networks](https://mlsanity.com/paper/1411.6191/) (2014) - [Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets](https://mlsanity.com/paper/1704.07121/) (2017) ## Built on by (most cited first, 9 of 9) - [Can Language Models Learn Typologically Implausible Languages?](https://mlsanity.com/paper/2502.12317/) (2025) - [BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models](https://mlsanity.com/paper/2309.06085/) (2023) - [Large Language Models as Proxies for Theories of Human Linguistic Cognition](https://mlsanity.com/paper/2502.07687/) (2025) - [A systematic framework for generating novel experimental hypotheses from language models](https://mlsanity.com/paper/2408.05086/) (2024) - [No Such Thing as a General Learner: Language models and their dual optimization](https://mlsanity.com/paper/2408.09544/) (2024) - [Non-native speakers of English or ChatGPT: Who thinks better?](https://mlsanity.com/paper/2412.00457/) (2024) - [LLMs as Function Approximators: Terminology, Taxonomy, and Questions for Evaluation](https://mlsanity.com/paper/2407.13744/) (2024) - [On the Compatibility of Generative AI and Generative Linguistics](https://mlsanity.com/paper/2411.10533/) (2024) - [Generative Linguistics, Large Language Models, and the Social Nature of Scientific Success](https://mlsanity.com/paper/2503.20088/) (2025) ## Abstract (arXiv) We present a critical assessment of Piantadosi's (2023) claim that "Modern language models refute Chomsky's approach to language," focusing on four main points. First, despite the impressive performance and utility of large language models (LLMs), humans achieve their capacity for language after exposure to several orders of magnitude less data. The fact that young children become competent, fluent speakers of their native languages with relatively little exposure to them is the central mystery of language learning to which Chomsky initially drew attention, and LLMs currently show little promise of solving this mystery. Second, what can the artificial reveal about the natural? Put simply, the implications of LLMs for our understanding of the cognitive structures and mechanisms underlying language and its acquisition are like the implications of airplanes for understanding how birds fly. Third, LLMs cannot constitute scientific theories of language for several reasons, not least of which is that scientific theories must provide interpretable explanations, not just predictions. This leads to our final point: to even determine whether the linguistic and cognitive capabilities of LLMs rival those of humans requires explicating what humans' capacities actually are. In other words, it requires a separate theory of language and cognition; generative linguistics provides precisely such a theory. As such, we conclude that generative linguistics as a scientific discipline will remain indispensable throughout the 21st century and beyond.