Model Connectomes: A Generational Approach to Data-Efficient Language Models | Paper Citation Record

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This paper cites Love, Christopher J.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Love, Christopher J

Reference 1

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This paper cites Language in brains, minds, and machines.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Language in brains, minds, and machines

Reference 2

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This paper cites Catalyzing next-generation artificial intelligence through neuroai.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Catalyzing next-generation artificial intelligence through neuroai

Reference 3

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This paper cites A deep learning framework for neuroscience.

Model Connectomes: A Generational Approach to Data-Efficient Language Models A deep learning framework for neuroscience

Reference 4

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This paper cites How learning can guide evolution.Complex Systems, 1(3):495–502, 1987.

Model Connectomes: A Generational Approach to Data-Efficient Language Models How learning can guide evolution.Complex Systems, 1(3):495–502, 1987

Reference 5

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This paper cites A critique of pure learning and what artificial neural networks can learn from animal brains.

Model Connectomes: A Generational Approach to Data-Efficient Language Models A critique of pure learning and what artificial neural networks can learn from animal brains

Reference 6

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This paper cites Direct fit to nature: an evolutionary perspective on biological and artificial neural networks.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Direct fit to nature: an evolutionary perspective on biological and artificial neural networks

Reference 7

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This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 8

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This paper cites What artificial neural networks can tell us about human language acquisition.

Model Connectomes: A Generational Approach to Data-Efficient Language Models What artificial neural networks can tell us about human language acquisition

Reference 9

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This paper cites Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus

Reference 10

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This paper cites Improving language understanding by generative pre-training.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Improving language understanding by generative pre-training

Reference 11

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This paper cites Llama v3: Next-generation foundation language model, 2023.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Llama v3: Next-generation foundation language model, 2023

Reference 12

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This paper cites Deepseek llm: Advancing deep information retrieval with large language models, 2023.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Deepseek llm: Advancing deep information retrieval with large language models, 2023

Reference 13

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This paper cites Scaling laws for neural language mod- els, 2020.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Scaling laws for neural language mod- els, 2020

Reference 14

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This paper cites Incorporating context into language encoding models for fmri.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Incorporating context into language encoding models for fmri

Reference 15

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This paper cites Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain).

Model Connectomes: A Generational Approach to Data-Efficient Language Models Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)

Reference 16

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Model Connectomes: A Generational Approach to Data-Efficient Language Models Unresolved cited work

Reference 17

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This paper cites Brains and algorithms partially converge in natural language processing.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Brains and algorithms partially converge in natural language processing

Reference 18

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This paper cites Shared computational principles for language processing in humans and deep language models.Nature neuroscience, 25(3):369–380, 2022.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Shared computational principles for language processing in humans and deep language models.Nature neuroscience, 25(3):369–380, 2022

Reference 19

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This paper cites Gptq: Accu- rate post-training quantization for generative pre-trained transformers, 2022.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Gptq: Accu- rate post-training quantization for generative pre-trained transformers, 2022

Reference 20

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This paper cites Movement pruning: Adaptive sparsity by fine-tuning.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Movement pruning: Adaptive sparsity by fine-tuning

Reference 21

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This paper cites Le, Geoffrey Hinton, and Jeff Dean.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Le, Geoffrey Hinton, and Jeff Dean

Reference 22

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This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks.

Model Connectomes: A Generational Approach to Data-Efficient Language Models The lottery ticket hypothesis: Finding sparse, trainable neural networks

Reference 23

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This paper cites Unmasking the lottery ticket hypothesis: What’s encoded in a winning ticket’s mask? In Proceedings of the International Conference on Learning Representations (ICLR), 2023.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Unmasking the lottery ticket hypothesis: What’s encoded in a winning ticket’s mask? In Proceedings of the International Conference on Learning Representations (ICLR), 2023

Reference 24

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This paper cites Lottery ticket adaptation: Mitigating destructive interference in llms,.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Lottery ticket adaptation: Mitigating destructive interference in llms,

Reference 25

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This paper cites Lottery tickets in llms: Robustness to adversarial examples via binary masking.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Lottery tickets in llms: Robustness to adversarial examples via binary masking

Reference 26

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This paper cites Sparse winning tickets are data-efficient image recognizers.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Sparse winning tickets are data-efficient image recognizers

Reference 27

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This paper cites Pruning neural networks without any data by iteratively conserving synaptic flow.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 28

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This paper cites Deconstructing lottery tickets: Zeros, signs, and the supermask.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Deconstructing lottery tickets: Zeros, signs, and the supermask

Reference 29

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This paper cites Complex com- putation from developmental priors.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Complex com- putation from developmental priors

Reference 30

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This paper cites Encoding innate ability through a genomic bottleneck.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Encoding innate ability through a genomic bottleneck

Reference 31

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This paper cites Enhancing Interpretability using Human Similarity Judgements to Prune Word Embeddings.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Enhancing Interpretability using Human Similarity Judgements to Prune Word Embeddings

Reference 32

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This paper cites Pruning sparse features for cognitive modeling.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Pruning sparse features for cognitive modeling

Reference 33

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This paper cites American parenting of language-learning children: Persisting differences in family-child interactions observed in natural home environments.

Model Connectomes: A Generational Approach to Data-Efficient Language Models American parenting of language-learning children: Persisting differences in family-child interactions observed in natural home environments

Reference 34

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This paper cites The fineweb datasets: Decanting the web for the finest text data at scale.

Model Connectomes: A Generational Approach to Data-Efficient Language Models The fineweb datasets: Decanting the web for the finest text data at scale

Reference 35

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This paper cites modded-nanogpt: Speedrunning the nanogpt baseline, 2024.

Model Connectomes: A Generational Approach to Data-Efficient Language Models modded-nanogpt: Speedrunning the nanogpt baseline, 2024

Reference 36

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This paper cites Hellaswag: Can a machine really finish your sentence? In Proceedings of the AAAI Conference on Artificial Intelligence, pages 11867–11878.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Hellaswag: Can a machine really finish your sentence? In Proceedings of the AAAI Conference on Artificial Intelligence, pages 11867–11878

Reference 37

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This paper cites Measuring massive multitask language understanding, 2021.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Measuring massive multitask language understanding, 2021

Reference 38

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This paper cites On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior.

Model Connectomes: A Generational Approach to Data-Efficient Language Models On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior

Reference 39

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Model Connectomes: A Generational Approach to Data-Efficient Language Models Unresolved cited work

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This paper cites Word frequency and predictability dissociate in naturalistic reading.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Word frequency and predictability dissociate in naturalistic reading

Reference 41

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This paper cites A probabilistic earley parser as a psycholinguistic model.

Model Connectomes: A Generational Approach to Data-Efficient Language Models A probabilistic earley parser as a psycholinguistic model

Reference 42

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This paper cites The effect of word predictability on reading time is loga- rithmic.

Model Connectomes: A Generational Approach to Data-Efficient Language Models The effect of word predictability on reading time is loga- rithmic

Reference 43

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This paper cites The natural stories corpus: a reading-time corpus of english texts containing rare syntactic constructions.

Model Connectomes: A Generational Approach to Data-Efficient Language Models The natural stories corpus: a reading-time corpus of english texts containing rare syntactic constructions

Reference 44

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This paper cites Instruction-tuning Aligns LLMs to the Human Brain.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Instruction-tuning Aligns LLMs to the Human Brain

Reference 45

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This paper cites Brain-Like Language Processing via a Shallow Untrained Multihead Attention Network.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Brain-Like Language Processing via a Shallow Untrained Multihead Attention Network

Reference 46

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This paper cites Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens

Reference 47

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This paper cites Large GPT-like Models are Bad Babies: A Closer Look at the Relationship between Linguistic Competence and Psycholinguistic Measures.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Large GPT-like Models are Bad Babies: A Closer Look at the Relationship between Linguistic Competence and Psycholinguistic Measures

Reference 48

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This paper cites Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times

Reference 49

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This paper cites Scaling in cognitive modelling: A multilingual approach to human reading times.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Scaling in cognitive modelling: A multilingual approach to human reading times

Reference 50

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This paper cites New method for fmri investigations of language: defining rois functionally in individual subjects.

Model Connectomes: A Generational Approach to Data-Efficient Language Models New method for fmri investigations of language: defining rois functionally in individual subjects

Reference 51

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This paper cites Driving and suppressing the human lan- guage network using large language models.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Driving and suppressing the human lan- guage network using large language models

Reference 52

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This paper cites The LLM Language Network: A Neuroscientific Approach for Identifying Causally Task-Relevant Units.

Model Connectomes: A Generational Approach to Data-Efficient Language Models The LLM Language Network: A Neuroscientific Approach for Identifying Causally Task-Relevant Units

Reference 53

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This paper cites Distilling the Knowledge in a Neural Network.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Distilling the Knowledge in a Neural Network

Reference 54

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This paper cites Big self-supervised models are strong semi-supervised learners.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Big self-supervised models are strong semi-supervised learners

Reference 55

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This paper cites PathNet: Evolution Channels Gradient Descent in Super Neural Networks.

Model Connectomes: A Generational Approach to Data-Efficient Language Models PathNet: Evolution Channels Gradient Descent in Super Neural Networks

Reference 56

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This paper cites Learning both weights and connections for efficient neural networks.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Learning both weights and connections for efficient neural networks

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:38:38.206720Z

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This paper cites Synaptic density in human frontal cortex-developmental changes and effects of aging.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Synaptic density in human frontal cortex-developmental changes and effects of aging

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:38:38.191754Z

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This paper cites Natural evolution strategies.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Natural evolution strategies

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:38:38.151551Z

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This paper cites Designing neural net- works through neuroevolution.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Designing neural net- works through neuroevolution

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:38:38.058859Z

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This paper cites Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

Reference 61

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This paper cites Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

Reference 62

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This paper cites Root mean square layer normalization, 2019.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Root mean square layer normalization, 2019

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:38:38.011109Z

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This paper cites Roformer: Enhanced transformer with rotary position embedding.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Roformer: Enhanced transformer with rotary position embedding

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:38:37.998646Z

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This paper cites Brain-score: Which artificial neural network for object recognition is most brain-like? BioRxiv, page 407007, 2018.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Brain-score: Which artificial neural network for object recognition is most brain-like? BioRxiv, page 407007, 2018

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-16T05:38:37.525087Z

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This paper cites Hosseini, Nancy Kanwisher, Joshua B.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Hosseini, Nancy Kanwisher, Joshua B

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:38:37.975310Z

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This paper cites Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs.

Model Connectomes: A Generational Approach to Data-Efficient Language Models Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs

Reference 2024

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