mlsanityData to 7 Oct 2026

Large language models are not about natural language

arXiv 2512.13441 ↗First posted 15 Dec 2025cs.CL q-bio.NCcited by 0

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Abstract (arXiv)

Large Language Models are useless for linguistics, as they are probabilistic models that require a vast amount of data to analyse externalized strings of words. In contrast, human language is underpinned by a mind-internal computational system that recursively generates hierarchical thought structures. The language system grows with minimal external input and can readily distinguish between real language and impossible languages.

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References · 98

  1. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 789
  2. 2021Deduplicating Training Data Makes Language Models Bettercited by 705
  3. 2019BLiMP: The Benchmark of Linguistic Minimal Pairs for Englishcited by 553
  4. 2024The Platonic Representation Hypothesiscited by 362
  5. 2016Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependenciescited by 350
  6. 2019Theoretical Limitations of Self-Attention in Neural Sequence Modelscited by 286
  7. 2020Inductive Biases for Deep Learning of Higher-Level Cognitioncited by 273
  8. 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 264
  9. 2023Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretabilitycited by 194
  10. 2023Power Hungry Processing: Watts Driving the Cost of AI Deployment?cited by 186
  11. 2022The Debate Over Understanding in AI's Large Language Modelscited by 142
  12. 2019Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic Statecited by 137
38 more
  1. 2021How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVENcited by 124
  2. 2021Systematic Inequalities in Language Technology Performance across the World's Languagescited by 118
  3. 2018On the Practical Computational Power of Finite Precision RNNs for Language Recognitioncited by 117
  4. 2025On The Landscape of Spoken Language Models: A Comprehensive Surveycited by 116
  5. 2022Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?cited by 111
  6. 2018What do RNN Language Models Learn about Filler-Gap Dependencies?cited by 86
  7. 2025Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corporacited by 86
  8. 2023Physics of Language Models: Part 1, Learning Hierarchical Language Structurescited by 83
  9. 2022Training Trajectories of Language Models Across Scalescited by 80
  10. 2023Testing the Predictions of Surprisal Theory in 11 Languagescited by 75
  11. 2024Why are Sensitive Functions Hard for Transformers?cited by 74
  12. 2023How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Modelcited by 70
  13. 2022A fine-grained comparison of pragmatic language understanding in humans and language modelscited by 69
  14. 2016Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learnercited by 68
  15. 2024Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNscited by 65
  16. 2023What Do Self-Supervised Speech Models Know About Words?cited by 62
  17. 2020RNNs can generate bounded hierarchical languages with optimal memorycited by 59
  18. 2025Large Language Models Share Representations of Latent Grammatical Concepts Across Typologically Diverse Languagescited by 57
  19. 2023Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generatorscited by 56
  20. 2025Deep Learning is Not So Mysterious or Differentcited by 55
  21. 2024Language models align with human judgments on key grammatical constructionscited by 49
  22. 2024People cannot distinguish GPT-4 from a human in a Turing testcited by 46
  23. 2025What Has a Foundation Model Found? Using Inductive Bias to Probe for World Modelscited by 46
  24. 2023Testing AI on language comprehension tasks reveals insensitivity to underlying meaningcited by 45
  25. 2015Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortexcited by 44
  26. 2022Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systemscited by 44
  27. 2021Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERTcited by 41
  28. 2021Word Acquisition in Neural Language Modelscited by 41
  29. 2023Modeling rapid language learning by distilling Bayesian priors into artificial neural networkscited by 40
  30. 2024Towards a theory of how the structure of language is acquired by deep neural networkscited by 38
  31. 2017Deep Learning Models of the Retinal Response to Natural Scenescited by 36
  32. 2019On the Computational Power of RNNscited by 35
  33. 2023Probing self-supervised speech models for phonetic and phonemic information: a case study in aspirationcited by 35
  34. 2024Filtered Corpus Training (FiCT) Shows that Language Models can Generalize from Indirect Evidencecited by 35
  35. 2023Characterizing Learning Curves During Language Model Pre-Training: Learning, Forgetting, and Stabilitycited by 34
  36. 2025The Non-Linear Representation Dilemma: Is Causal Abstraction Enough for Mechanistic Interpretability?cited by 34
  37. 2018Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networkscited by 33
  38. 2023A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learningcited by 32

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