# Mission: Impossible Language Models

arXiv: https://arxiv.org/abs/2401.06416 · HTML: https://mlsanity.com/paper/2401.06416/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 76 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 30 of 32)

- [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)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [Causal Abstractions of Neural Networks](https://mlsanity.com/paper/2106.02997/) (2021)
- [The Impact of Positional Encoding on Length Generalization in Transformers](https://mlsanity.com/paper/2305.19466/) (2023)
- [Theoretical Limitations of Self-Attention in Neural Sequence Models](https://mlsanity.com/paper/1906.06755/) (2019)
- [Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/1808.09031/) (2018)
- [Neural Networks and the Chomsky Hierarchy](https://mlsanity.com/paper/2207.02098/) (2022)
- [Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations](https://mlsanity.com/paper/2303.02536/) (2023)
- [Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little](https://mlsanity.com/paper/2104.06644/) (2021)
- [A Systematic Assessment of Syntactic Generalization in Neural Language Models](https://mlsanity.com/paper/2005.03692/) (2020)
- [Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State](https://mlsanity.com/paper/1903.03260/) (2019)
- [On the Practical Computational Power of Finite Precision RNNs for Language Recognition](https://mlsanity.com/paper/1805.04908/) (2018)
- [What do RNN Language Models Learn about Filler-Gap Dependencies?](https://mlsanity.com/paper/1809.00042/) (2018)
- [Out of Order: How Important Is The Sequential Order of Words in a Sentence in Natural Language Understanding Tasks?](https://mlsanity.com/paper/2012.15180/) (2020)
- [A Formal Hierarchy of RNN Architectures](https://mlsanity.com/paper/2004.08500/) (2020)
- [Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus](https://mlsanity.com/paper/2301.11796/) (2023)
- [RNNs can generate bounded hierarchical languages with optimal memory](https://mlsanity.com/paper/2010.07515/) (2020)
- [Sequential Neural Networks as Automata](https://mlsanity.com/paper/1906.01615/) (2019)
- [How Can Self-Attention Networks Recognize Dyck-n Languages?](https://mlsanity.com/paper/2010.04303/) (2020)
- [Injecting structural hints: Using language models to study inductive biases in language learning](https://mlsanity.com/paper/2304.13060/) (2023)
- [Word Order Does Matter (And Shuffled Language Models Know It)](https://mlsanity.com/paper/2203.10995/) (2022)
- [Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models](https://mlsanity.com/paper/1909.10579/) (2019)
- [Causal Proxy Models for Concept-Based Model Explanations](https://mlsanity.com/paper/2209.14279/) (2022)
- [When classifying grammatical role, BERT doesn't care about word order... except when it matters](https://mlsanity.com/paper/2203.06204/) (2022)
- [Pushdown Layers: Encoding Recursive Structure in Transformer Language Models](https://mlsanity.com/paper/2310.19089/) (2023)
- [Context-Free Transductions with Neural Stacks](https://mlsanity.com/paper/1809.02836/) (2018)
- [Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models](https://mlsanity.com/paper/2104.07578/) (2021)
- [What makes a language easy to deep-learn? Deep neural networks and humans similarly benefit from compositional structure](https://mlsanity.com/paper/2302.12239/) (2023)

## Built on by (most cited first, 30 of 32)

- [How Linguistics Learned to Stop Worrying and Love the Language Models](https://mlsanity.com/paper/2501.17047/) (2025)
- [Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models](https://mlsanity.com/paper/2412.02980/) (2024)
- [Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens](https://mlsanity.com/paper/2504.14666/) (2025)
- [Can Language Models Learn Typologically Implausible Languages?](https://mlsanity.com/paper/2502.12317/) (2025)
- [Endless Jailbreaks with Bijection Learning](https://mlsanity.com/paper/2410.01294/) (2024)
- [Both Direct and Indirect Evidence Contribute to Dative Alternation Preferences in Language Models](https://mlsanity.com/paper/2503.20850/) (2025)
- [gzip Predicts Data-dependent Scaling Laws](https://mlsanity.com/paper/2405.16684/) (2024)
- [Language in Vivo vs. in Silico: Size Matters but Larger Language Models Still Do Not Comprehend Language on a Par with Humans Due to Impenetrable Semantic Reference](https://mlsanity.com/paper/2404.14883/) (2024)
- [Kallini et al. (2024) do not compare impossible languages with constituency-based ones](https://mlsanity.com/paper/2410.12271/) (2024)
- [Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects](https://mlsanity.com/paper/2406.15955/) (2024)
- [Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention](https://mlsanity.com/paper/2405.16042/) (2024)
- [Large Language Models as Proxies for Theories of Human Linguistic Cognition](https://mlsanity.com/paper/2502.07687/) (2025)
- [Shaping Shared Languages: Human and Large Language Models' Inductive Biases in Emergent Communication](https://mlsanity.com/paper/2503.04395/) (2025)
- [Linguistic Structure from a Bottleneck on Sequential Information Processing](https://mlsanity.com/paper/2405.12109/) (2024)
- [Bringing Up a Bilingual BabyLM: Investigating Multilingual Language Acquisition Using Small-Scale Models](https://mlsanity.com/paper/2603.29552/) (2026)
- [Unnatural Languages Are Not Bugs but Features for LLMs](https://mlsanity.com/paper/2503.01926/) (2025)
- [Reading the unreadable: Creating a dataset of 19th century English newspapers using image-to-text language models](https://mlsanity.com/paper/2502.14901/) (2025)
- [Removing Noise, not Finding Gold: Quality Filtering for Large-Scale Pretraining](https://mlsanity.com/paper/2510.00866/) (2025)
- [Scrambled text: training Language Models to correct OCR errors using synthetic data](https://mlsanity.com/paper/2409.19735/) (2024)
- [Baby Scale: Investigating Models Trained on Individual Children's Language Input](https://mlsanity.com/paper/2603.29522/) (2026)
- [Structure Before Collapse: Transient semantic geometry in next-token prediction](https://mlsanity.com/paper/2606.26749/) (2026)
- [Probability Consistency in Large Language Models: Theoretical Foundations Meet Empirical Discrepancies](https://mlsanity.com/paper/2505.08739/) (2025)
- [Pragmatics beyond humans: meaning, communication, and LLMs](https://mlsanity.com/paper/2508.06167/) (2025)
- [Language models as tools for investigating the distinction between possible and impossible natural languages](https://mlsanity.com/paper/2512.09394/) (2025)
- [Large language models have learned to use language](https://mlsanity.com/paper/2512.12447/) (2025)
- [Large language models are not about natural language](https://mlsanity.com/paper/2512.13441/) (2025)
- [Linguists should learn to love speech-based deep learning models](https://mlsanity.com/paper/2512.14506/) (2025)
- [Are Language Models Models?](https://mlsanity.com/paper/2601.10421/) (2026)
- [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)
- [Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilities](https://mlsanity.com/paper/2604.09466/) (2026)

## Abstract (arXiv)

Chomsky and others have very directly claimed that large language models (LLMs) are equally capable of learning languages that are possible and impossible for humans to learn. However, there is very little published experimental evidence to support such a claim. Here, we develop a set of synthetic impossible languages of differing complexity, each designed by systematically altering English data with unnatural word orders and grammar rules. These languages lie on an impossibility continuum: at one end are languages that are inherently impossible, such as random and irreversible shuffles of English words, and on the other, languages that may not be intuitively impossible but are often considered so in linguistics, particularly those with rules based on counting word positions. We report on a wide range of evaluations to assess the capacity of GPT-2 small models to learn these uncontroversially impossible languages, and crucially, we perform these assessments at various stages throughout training to compare the learning process for each language. Our core finding is that GPT-2 struggles to learn impossible languages when compared to English as a control, challenging the core claim. More importantly, we hope our approach opens up a productive line of inquiry in which different LLM architectures are tested on a variety of impossible languages in an effort to learn more about how LLMs can be used as tools for these cognitive and typological investigations.