mlsanityData to 14 Oct 2026

The Natural Stories Corpus

arXiv 1708.05763 ↗First posted 18 Aug 2017cs.CLcited by 40

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Abstract (arXiv)

It is now a common practice to compare models of human language processing by predicting participant reactions (such as reading times) to corpora consisting of rich naturalistic linguistic materials. However, many of the corpora used in these studies are based on naturalistic text and thus do not contain many of the low-frequency syntactic constructions that are often required to distinguish processing theories. Here we describe a new corpus consisting of English texts edited to contain many low-frequency syntactic constructions while still sounding fluent to native speakers. The corpus is annotated with hand-corrected parse trees and includes self-paced reading time data. Here we give an overview of the content of the corpus and release the data.

Key facts. Cited by 40 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Aug 2017.

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Citing papers per year: 2021: 1; 2022: 6; 2023: 6; 2024: 9; 2025: 11; 2026: 7.

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  1. 2022Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?cited by 113
  2. 2022What Artificial Neural Networks Can Tell Us About Human Language Acquisitioncited by 99
  3. 2021Revisiting the Uniform Information Density Hypothesiscited by 68
  4. 2023Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokenscited by 41
  5. 2023Instruction-tuning Aligns LLMs to the Human Braincited by 40
  6. 2024Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Timescited by 39
  7. 2024How to Compute the Probability of a Wordcited by 36
  8. 2024Leading Whitespaces of Language Models' Subword Vocabulary Pose a Confound for Calculating Word Probabilitiescited by 30
  9. 2025Large Language Models Are Human-Like Internallycited by 29
  10. 2024Generalized Measures of Anticipation and Responsivity in Online Language Processingcited by 24
  11. 2022On the Effect of Anticipation on Reading Timescited by 19
  12. 2022Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisalcited by 14
28 more
  1. 2024Linear Recency Bias During Training Improves Transformers' Fit to Reading Timescited by 14
  2. 2023Words, Subwords, and Morphemes: What Really Matters in the Surprisal-Reading Time Relationship?cited by 13
  3. 2023Large GPT-like Models are Bad Babies: A Closer Look at the Relationship between Linguistic Competence and Psycholinguistic Measurescited by 11
  4. 2025To model human linguistic prediction, make LLMs less superhumancited by 11
  5. 2022Analyzing Wrap-Up Effects through an Information-Theoretic Lenscited by 10
  6. 2024What Are Large Language Models Mapping to in the Brain? A Case Against Over-Reliance on Brain Scorescited by 10
  7. 2025Language Models Grow Less Humanlike beyond Phase Transitioncited by 10
  8. 2024Towards a Similarity-adjusted Surprisal Theorycited by 9
  9. 2023Temperature-scaling surprisal estimates improve fit to human reading times -- but does it do so for the "right reasons"?cited by 7
  10. 2024Revenge of the Fallen? Recurrent Models Match Transformers at Predicting Human Language Comprehension Metricscited by 6
  11. 2024The Impact of Token Granularity on the Predictive Power of Language Model Surprisalcited by 5
  12. 2025The Inverse Scaling Effect of Pre-Trained Language Model Surprisal Is Not Due to Data Leakagecited by 5
  13. 2025Toward a Theory of Generalizability in LLM Mechanistic Interpretability Researchcited by 5
  14. 2023Psychometric Predictive Power of Large Language Modelscited by 4
  15. 2025How Well Does First-Token Entropy Approximate Word Entropy as a Psycholinguistic Predictor?cited by 4
  16. 2025Human-like fleeting memory improves language learning but impairs reading time prediction in transformer language modelscited by 3
  17. 2025Strategic resource allocation in memory encoding: An efficiency principle shaping language processingcited by 2
  18. 2022Fusing Sentence Embeddings Into LSTM-based Autoregressive Language Modelscited by 1
  19. 2025Vectors from Larger Language Models Predict Human Reading Time and fMRI Data More Poorly when Dimensionality Expansion is Controlledcited by 1
  20. 2025Surprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorlycited by 1
  21. 2026Information-Theoretic Storage Cost in Sentence Comprehensioncited by 1
  22. 2026Syntactically-guided Information Maintenance in Sentence Comprehensioncited by 1
  23. 2025Model Connectomes: A Generational Approach to Data-Efficient Language Modelscited by 0
  24. 2026To Words and Beyond: Probing Large Language Models for Sentence-Level Psycholinguistic Norms of Memorability and Reading Timescited by 0
  25. 2026Readers make targeted regressions to plausible errors in reanalysis of "noisy-channel garden-path" sentencescited by 0
  26. 2026Heterogeneous Neural Predictivity from Language Models During Naturalistic Comprehensioncited by 0
  27. 2026Timesteps of Mamba Align with Human Reading Timescited by 0
  28. 2026Using LMs to Model the Effects of Context and Coreference during Sentence Comprehensioncited by 0