Discovering Chunks in Neural Embeddings for Interpretability | Paper Citation Record

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This paper cites and Berrada, M.

Discovering Chunks in Neural Embeddings for Interpretability and Berrada, M

Reference 1

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This paper cites and Srikant, R.

Discovering Chunks in Neural Embeddings for Interpretability and Srikant, R

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Discovering Chunks in Neural Embeddings for Interpretability and richard baraniuk

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This paper cites Revisiting model stitching to compare neural representations.

Discovering Chunks in Neural Embeddings for Interpretability Revisiting model stitching to compare neural representations

Reference 4

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This paper cites Extrapolative-Interpolative Cycle-Consistency Learning for Video Frame Extrapolation.

Discovering Chunks in Neural Embeddings for Interpretability Extrapolative-Interpolative Cycle-Consistency Learning for Video Frame Extrapolation

Reference 5

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This paper cites Probing classifiers: Promises, shortcomings, and advances.

Discovering Chunks in Neural Embeddings for Interpretability Probing classifiers: Promises, shortcomings, and advances

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This paper cites Eliciting Latent Predictions from Transformers with the Tuned Lens.

Discovering Chunks in Neural Embeddings for Interpretability Eliciting Latent Predictions from Transformers with the Tuned Lens

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This paper cites Natural Language Processing with Python.

Discovering Chunks in Neural Embeddings for Interpretability Natural Language Processing with Python

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This paper cites Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning.

Discovering Chunks in Neural Embeddings for Interpretability Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning

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This paper cites L., Anil, C., Denison, C., Askell, A., Lasenby, R., Wu, Y., Kravec, S., Schiefer, N., Maxwell, T., Joseph, N., Tamkin, A., Nguyen, K., McLean, B., Burke, J.

Discovering Chunks in Neural Embeddings for Interpretability L., Anil, C., Denison, C., Askell, A., Lasenby, R., Wu, Y., Kravec, S., Schiefer, N., Maxwell, T., Joseph, N., Tamkin, A., Nguyen, K., McLean, B., Burke, J

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Discovering Chunks in Neural Embeddings for Interpretability Unresolved cited work

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This paper cites Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Small.

Discovering Chunks in Neural Embeddings for Interpretability Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Small

Reference 12

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This paper cites M., Cunningham, J.

Discovering Chunks in Neural Embeddings for Interpretability M., Cunningham, J

Reference 13

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Discovering Chunks in Neural Embeddings for Interpretability Unresolved cited work

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This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

Discovering Chunks in Neural Embeddings for Interpretability Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 15

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This paper cites Knowledge neurons in pretrained transformers.

Discovering Chunks in Neural Embeddings for Interpretability Knowledge neurons in pretrained transformers

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This paper cites Jump to Conclusions: Short-Cutting Transformers With Linear Transformations.

Discovering Chunks in Neural Embeddings for Interpretability Jump to Conclusions: Short-Cutting Transformers With Linear Transformations

Reference 17

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This paper cites Rosetta Neurons: Mining the Common Units in a Model Zoo.

Discovering Chunks in Neural Embeddings for Interpretability Rosetta Neurons: Mining the Common Units in a Model Zoo

Reference 18

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This paper cites The Llama 3 Herd of Models.

Discovering Chunks in Neural Embeddings for Interpretability The Llama 3 Herd of Models

Reference 19

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Discovering Chunks in Neural Embeddings for Interpretability Unresolved cited work

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This paper cites Toy models of superposition.

Discovering Chunks in Neural Embeddings for Interpretability Toy models of superposition

Reference 21

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This paper cites K., Fries, P., and Singer, W.

Discovering Chunks in Neural Embeddings for Interpretability K., Fries, P., and Singer, W

Reference 23

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This paper cites A new algorithm for data compression.

Discovering Chunks in Neural Embeddings for Interpretability A new algorithm for data compression

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This paper cites Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space.

Discovering Chunks in Neural Embeddings for Interpretability Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space

Reference 25

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This paper cites C., Croker, S., Cheng, P.

Discovering Chunks in Neural Embeddings for Interpretability C., Croker, S., Cheng, P

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This paper cites Finding Neurons in a Haystack: Case Studies with Sparse Probing.

Discovering Chunks in Neural Embeddings for Interpretability Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 29

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Discovering Chunks in Neural Embeddings for Interpretability The Platonic Representation Hypothesis

Reference 31

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This paper cites What does BERT learn about the structure of language? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp.\ 3651--3657, 2019.

Discovering Chunks in Neural Embeddings for Interpretability What does BERT learn about the structure of language? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp.\ 3651--3657, 2019

Reference 32

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Discovering Chunks in Neural Embeddings for Interpretability Evaluating Sparse Autoencoders on Targeted Concept Erasure Tasks

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Discovering Chunks in Neural Embeddings for Interpretability and Hoffmann, J

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Discovering Chunks in Neural Embeddings for Interpretability Similarity of neural network representations revisited

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Discovering Chunks in Neural Embeddings for Interpretability E., Rosenbloom, P

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This paper cites Understanding image representations by measuring their equivariance and equivalence.

Discovering Chunks in Neural Embeddings for Interpretability Understanding image representations by measuring their equivariance and equivalence

Reference 37

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Discovering Chunks in Neural Embeddings for Interpretability The Mythos of Model Interpretability

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Discovering Chunks in Neural Embeddings for Interpretability P., Santorini, B., and Marcinkiewicz, M

Reference 39

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Discovering Chunks in Neural Embeddings for Interpretability Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

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Discovering Chunks in Neural Embeddings for Interpretability Unresolved cited work

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Discovering Chunks in Neural Embeddings for Interpretability and Neo, C

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Discovering Chunks in Neural Embeddings for Interpretability Explanation in Artificial Intelligence: Insights from the Social Sciences

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Discovering Chunks in Neural Embeddings for Interpretability Relative representations enable zero-shot latent space communication

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Discovering Chunks in Neural Embeddings for Interpretability Compositional Explanations of Neurons

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Discovering Chunks in Neural Embeddings for Interpretability Synthesizing the preferred inputs for neurons in neural networks via deep generator networks

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Discovering Chunks in Neural Embeddings for Interpretability An overview of early vision in inceptionv1

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Discovering Chunks in Neural Embeddings for Interpretability Zoom in: An interpretability method for deep neural networks

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Discovering Chunks in Neural Embeddings for Interpretability Future Lens: Anticipating Subsequent Tokens from a Single Hidden State

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Discovering Chunks in Neural Embeddings for Interpretability RISE: Randomized Input Sampling for Explanation of Black-box Models

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Discovering Chunks in Neural Embeddings for Interpretability Learning to Generate Reviews and Discovering Sentiment

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Discovering Chunks in Neural Embeddings for Interpretability "Why Should I Trust You?": Explaining the Predictions of Any Classifier

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Discovering Chunks in Neural Embeddings for Interpretability On linear identifiability of learned representations

Reference 54

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Discovering Chunks in Neural Embeddings for Interpretability E pluribus unum: From complexity, universality

Reference 55

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This paper cites Word equations: Inherently interpretable sparse word embeddings through sparse coding.

Discovering Chunks in Neural Embeddings for Interpretability Word equations: Inherently interpretable sparse word embeddings through sparse coding

Reference 56

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Discovering Chunks in Neural Embeddings for Interpretability Bert rediscovers the classical nlp pipeline

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Discovering Chunks in Neural Embeddings for Interpretability Neurons in Large Language Models: Dead, N-gram, Positional

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Discovering Chunks in Neural Embeddings for Interpretability Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

Reference 59

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This paper cites Large Language Models are Interpretable Learners.

Discovering Chunks in Neural Embeddings for Interpretability Large Language Models are Interpretable Learners

Reference 60

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Observation 7d0ec334-0f9a-4833-a783-1789fa26b0fe · outbound

This paper cites Learning Structure from the Ground up— Hierarchical Representation Learning by Chunking.

Discovering Chunks in Neural Embeddings for Interpretability Learning Structure from the Ground up— Hierarchical Representation Learning by Chunking

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:29:21.824167Z

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Observation 7e80c8ad-efd4-4924-ae57-ddbeaba5a427 · outbound

This paper cites Chunking as a rational solution to the speed–accuracy trade-off in a serial reaction time task.

Discovering Chunks in Neural Embeddings for Interpretability Chunking as a rational solution to the speed–accuracy trade-off in a serial reaction time task

Reference 62

Resolution: verified exact; doi, observed 2026-08-09T14:29:20.238219Z

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Observation df9b9885-d1a3-4b1b-97c1-a119bc78da9c · outbound

This paper cites Two types of motifs enhance human recall and generalization of long sequences.

Discovering Chunks in Neural Embeddings for Interpretability Two types of motifs enhance human recall and generalization of long sequences

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:29:21.809385Z

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Observation 684eaec9-3a86-4c99-9116-ef16d435cba3 · outbound

This paper cites an unresolved cited work.

Discovering Chunks in Neural Embeddings for Interpretability Unresolved cited work

Reference 64

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Observation 11921eb9-ece5-4e6a-816e-84506ce06321 · outbound

This paper cites and Zhu, S.-c.

Discovering Chunks in Neural Embeddings for Interpretability and Zhu, S.-c

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-09T14:29:20.172855Z

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Observation 61301cd9-96cf-4314-99b3-c2df66171161 · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

Discovering Chunks in Neural Embeddings for Interpretability Representation Engineering: A Top-Down Approach to AI Transparency

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-09T14:29:20.179016Z

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Observation 483ce5e1-57b9-46d1-860e-bcdf4e3d0173 · outbound

This paper cites write newline.

Discovering Chunks in Neural Embeddings for Interpretability write newline

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-09T14:29:20.184050Z

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