mlsanityData to 14 Oct 2026

Structural Similarities Between Language Models and Neural Response Measurements

arXiv 2306.01930 ↗First posted 2 Jun 2023cs.CL cs.AIcited by 6

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Abstract (arXiv)

Large language models (LLMs) have complicated internal dynamics, but induce representations of words and phrases whose geometry we can study. Human language processing is also opaque, but neural response measurements can provide (noisy) recordings of activation during listening or reading, from which we can extract similar representations of words and phrases. Here we study the extent to which the geometries induced by these representations, share similarities in the context of brain decoding. We find that the larger neural language models get, the more their representations are structurally similar to neural response measurements from brain imaging. Code is available at urlhttps://github.com/coastalcph/brainlm.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 17 of its 40 references that are in the map. First posted on arXiv 2 Jun 2023.

Builds on

17 of 40 references.

References · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
  4. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
  5. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  6. 2015Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Bookscited by 1,043
  7. 2017Word Translation Without Parallel Datacited by 487
  8. 2020The Pushshift Reddit Datasetcited by 292
  9. 2018A Simple Method for Commonsense Reasoningcited by 223
  10. 2022The Debate Over Understanding in AI's Large Language Modelscited by 142
  11. 2019Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)cited by 128
  12. 2021Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Colorcited by 119
5 more
  1. 2013Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networkscited by 84
  2. 2023Do Vision and Language Models Share Concepts? A Vector Space Alignment Studycited by 27
  3. 2020Picking BERT's Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysiscited by 18
  4. 2020Brain2Word: Decoding Brain Activity for Language Generationcited by 15
  5. 2019Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary Inductioncited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 1.

6 papers.

  1. 2024On the Societal Impact of Open Foundation Modelscited by 65
  2. 2025Language models align with brain regions that represent concepts across modalitiescited by 4
  3. 2024Can AI and humans genuinely communicate?cited by 1
  4. 2026Beyond Value Benchmarks: Measuring Value-Structure Alignment in Large Language Models via Symmetric Q-Sortscited by 1
  5. 2024On the Shape of Brainscores for Large Language Models (LLMs)cited by 0
  6. 2025What if Othello-Playing Language Models Could See?cited by 0