# What Can String Probability Tell Us About Grammaticality? arXiv: https://arxiv.org/abs/2510.16227 ยท HTML: https://mlsanity.com/paper/2510.16227/ ## Key facts - Cited by 16 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 53 references that are in the map. - First posted on arXiv 17 Oct 2025. ## Builds on (most cited first, 24 of 24) - [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://mlsanity.com/paper/2406.17557/) (2024) - [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018) - [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019) - [Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies](https://mlsanity.com/paper/1611.01368/) (2016) - [Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca](https://mlsanity.com/paper/2304.08177/) (2023) - [Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/1808.09031/) (2018) - [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) - [What Artificial Neural Networks Can Tell Us About Human Language Acquisition](https://mlsanity.com/paper/2208.07998/) (2022) - [The Generative AI Paradox: "What It Can Create, It May Not Understand"](https://mlsanity.com/paper/2311.00059/) (2023) - [UER: An Open-Source Toolkit for Pre-training Models](https://mlsanity.com/paper/1909.05658/) (2019) - [Auxiliary task demands mask the capabilities of smaller language models](https://mlsanity.com/paper/2404.02418/) (2024) - [Language models align with human judgments on key grammatical constructions](https://mlsanity.com/paper/2402.01676/) (2024) - [Prompting is not a substitute for probability measurements in large language models](https://mlsanity.com/paper/2305.13264/) (2023) - [On the proper role of linguistically-oriented deep net analysis in linguistic theorizing](https://mlsanity.com/paper/2106.08694/) (2021) - [Modeling rapid language learning by distilling Bayesian priors into artificial neural networks](https://mlsanity.com/paper/2305.14701/) (2023) - [Can language models handle recursively nested grammatical structures? A case study on comparing models and humans](https://mlsanity.com/paper/2210.15303/) (2022) - [CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model](https://mlsanity.com/paper/2003.01355/) (2020) - [SLING: Sino Linguistic Evaluation of Large Language Models](https://mlsanity.com/paper/2210.11689/) (2022) - [Evaluating Neural Language Models as Cognitive Models of Language Acquisition](https://mlsanity.com/paper/2310.20093/) (2023) - [A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese](https://mlsanity.com/paper/2411.06096/) (2024) - [What can linguistics and deep learning contribute to each other?](https://mlsanity.com/paper/1809.04179/) (2018) - [What Goes Into a LM Acceptability Judgment? Rethinking the Impact of Frequency and Length](https://mlsanity.com/paper/2411.02528/) (2024) - [The Causal Influence of Grammatical Gender on Distributional Semantics](https://mlsanity.com/paper/2311.18567/) (2023) ## Built on by (most cited first, 16 of 16) - [Information-Theoretic Storage Cost in Sentence Comprehension](https://mlsanity.com/paper/2602.18217/) (2026) - [Implicit Representations of Grammaticality in Language Models](https://mlsanity.com/paper/2605.05197/) (2026) - [Linear representations of grammaticality in neural language models](https://mlsanity.com/paper/2607.15175/) (2026) - [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) - [Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilities](https://mlsanity.com/paper/2604.09466/) (2026) - [You Can't Fight in Here! This is BBS!](https://mlsanity.com/paper/2604.09501/) (2026) - [Targeted Linguistic Analysis of Sign Language Models with Minimal Translation Pairs](https://mlsanity.com/paper/2604.27232/) (2026) - [Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear](https://mlsanity.com/paper/2605.10061/) (2026) - [Do Language Models Encode Knowledge of Linguistic Constraint Violations?](https://mlsanity.com/paper/2605.12055/) (2026) - [When transformers learn "impossible" languages, what do they learn?](https://mlsanity.com/paper/2606.30815/) (2026) - [A retrospective analysis on the use of LLMs to study infant syntax learning](https://mlsanity.com/paper/2609.26539/) (2026) - [Assessing the Impact of Language Disparity on Multilingual Linguistic Ability in Large Language Models](https://mlsanity.com/paper/2610.00540/) (2026) ## Abstract (arXiv) What have language models (LMs) learned about grammar? This question remains hotly debated, with major ramifications for linguistic theory. However, since probability and grammaticality are distinct notions in linguistics, it is not obvious what string probabilities can reveal about an LM's underlying grammatical knowledge. We present a theoretical analysis of the relationship between grammar, meaning, and string probability, based on simple assumptions about the generative process of corpus data. Our framework makes three predictions, which we validate empirically using 280K sentence pairs in English and Chinese: (1) correlation between the probability of strings within minimal pairs, i.e., string pairs with minimal semantic differences; (2) correlation between models' and humans' deltas within minimal pairs; and (3) poor separation in probability space between unpaired grammatical and ungrammatical strings. Our analyses give theoretical grounding for using probability to learn about LMs' structural knowledge, and suggest directions for future work in LM grammatical evaluation.