Finding Neurons in a Haystack: Case Studies with Sparse Probing | Paper Citation Record

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Towards Best Practices of Activation Patching in Language Models: Metrics and Methods cites this paper.

Towards Best Practices of Activation Patching in Language Models: Metrics and Methods Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 85

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Scaling and evaluating sparse autoencoders cites this paper.

Scaling and evaluating sparse autoencoders Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 18

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PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model cites this paper.

PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Understanding Multimodal LLMs: the Mechanistic Interpretability of Llava in Visual Question Answering cites this paper.

Understanding Multimodal LLMs: the Mechanistic Interpretability of Llava in Visual Question Answering Finding Neurons in a Haystack: Case Studies with Sparse Probing

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A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions cites this paper.

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 69

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GPT-2 Through the Lens of Vector Symbolic Architectures cites this paper.

GPT-2 Through the Lens of Vector Symbolic Architectures Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Joint Knowledge Editing for Information Enrichment and Probability Promotion cites this paper.

Joint Knowledge Editing for Information Enrichment and Probability Promotion Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 9

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Flash Interpretability: Decoding Specialised Feature Neurons in Large Language Models with the LM-Head cites this paper.

Flash Interpretability: Decoding Specialised Feature Neurons in Large Language Models with the LM-Head Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words cites this paper.

Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 12

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Transcoders Beat Sparse Autoencoders for Interpretability cites this paper.

Transcoders Beat Sparse Autoencoders for Interpretability Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 25

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Partially Rewriting a Transformer in Natural Language cites this paper.

Partially Rewriting a Transformer in Natural Language Finding Neurons in a Haystack: Case Studies with Sparse Probing

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What is a Number, That a Large Language Model May Know It? cites this paper.

What is a Number, That a Large Language Model May Know It? Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Discovering Chunks in Neural Embeddings for Interpretability cites this paper.

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

Reference 29

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Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution cites this paper.

Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models cites this paper.

Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 10

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings cites this paper.

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 18

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Representation Learning on a Random Lattice cites this paper.

Representation Learning on a Random Lattice Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Investigating task-specific prompts and sparse autoencoders for activation monitoring cites this paper.

Investigating task-specific prompts and sparse autoencoders for activation monitoring Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 9

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Polysemy of Synthetic Neurons Towards a New Type of Explanatory Categorical Vector Spaces cites this paper.

Polysemy of Synthetic Neurons Towards a New Type of Explanatory Categorical Vector Spaces Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 50

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Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints cites this paper.

Recovering Event Probabilities from Large Language Model Embeddings via Axiomatic Constraints Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 16

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Emergent Specialization: Rare Token Neurons in Language Models cites this paper.

Emergent Specialization: Rare Token Neurons in Language Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 14

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Void in Language Models cites this paper.

Void in Language Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Understanding Gated Neurons in Transformers from Their Input-Output Functionality cites this paper.

Understanding Gated Neurons in Transformers from Their Input-Output Functionality Finding Neurons in a Haystack: Case Studies with Sparse Probing

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ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models cites this paper.

ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipeline cites this paper.

Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipeline Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Pretrained LLMs Learn Multiple Types of Uncertainty cites this paper.

Pretrained LLMs Learn Multiple Types of Uncertainty Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Understanding the learned look-ahead behavior of chess neural networks cites this paper.

Understanding the learned look-ahead behavior of chess neural networks Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Distinct Computations Emerge From Compositional Curricula in In-Context Learning cites this paper.

Distinct Computations Emerge From Compositional Curricula in In-Context Learning Finding Neurons in a Haystack: Case Studies with Sparse Probing

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The Compositional Architecture of Regret in Large Language Models cites this paper.

The Compositional Architecture of Regret in Large Language Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Cross-Layer Discrete Concept Discovery for Interpreting Language Models cites this paper.

Cross-Layer Discrete Concept Discovery for Interpreting Language Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Learning to Skip the Middle Layers of Transformers cites this paper.

Learning to Skip the Middle Layers of Transformers Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Scaling laws for activation steering with Llama 2 models and refusal mechanisms cites this paper.

Scaling laws for activation steering with Llama 2 models and refusal mechanisms Finding Neurons in a Haystack: Case Studies with Sparse Probing

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On the transferability of Sparse Autoencoders for interpreting compressed models cites this paper.

On the transferability of Sparse Autoencoders for interpreting compressed models Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Adaptive Lattice-based Motion Planning cites this paper.

Adaptive Lattice-based Motion Planning Finding Neurons in a Haystack: Case Studies with Sparse Probing

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NEAT: Concept driven Neuron Attribution in LLMs cites this paper.

NEAT: Concept driven Neuron Attribution in LLMs Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Distribution-Aware Feature Selection for SAEs cites this paper.

Distribution-Aware Feature Selection for SAEs Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework cites this paper.

Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Towards Atoms of Large Language Models cites this paper.

Towards Atoms of Large Language Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Foundation Models for Discovery and Exploration in Chemical Space cites this paper.

Foundation Models for Discovery and Exploration in Chemical Space Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 266

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Friends and Grandmothers in Silico: Localizing Entity Cells in Language Models cites this paper.

Friends and Grandmothers in Silico: Localizing Entity Cells in Language Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Do Audio-Visual Large Language Models Really See and Hear? cites this paper.

Do Audio-Visual Large Language Models Really See and Hear? Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 19

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Darkness Visible: Reading the Exception Handler of a Language Model cites this paper.

Darkness Visible: Reading the Exception Handler of a Language Model Finding Neurons in a Haystack: Case Studies with Sparse Probing

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I Walk the Line: Examining the Role of Gestalt Continuity in Object Binding for Vision Transformers cites this paper.

I Walk the Line: Examining the Role of Gestalt Continuity in Object Binding for Vision Transformers Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Latent Instruction Representation Alignment: defending against jailbreaks, backdoors and undesired knowledge in LLMs cites this paper.

Latent Instruction Representation Alignment: defending against jailbreaks, backdoors and undesired knowledge in LLMs Finding Neurons in a Haystack: Case Studies with Sparse Probing

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There Will Be a Scientific Theory of Deep Learning cites this paper.

There Will Be a Scientific Theory of Deep Learning Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 207

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Perturbation Probing: A Two-Pass-per-Prompt Diagnostic for FFN Behavioral Circuits in Aligned LLMs cites this paper.

Perturbation Probing: A Two-Pass-per-Prompt Diagnostic for FFN Behavioral Circuits in Aligned LLMs Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 11

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Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders cites this paper.

Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 11

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Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders cites this paper.

Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 11

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A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models cites this paper.

A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

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WriteSAE: Sparse Autoencoders for Recurrent State cites this paper.

WriteSAE: Sparse Autoencoders for Recurrent State Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 92

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WriteSAE: Sparse Autoencoders for Recurrent State cites this paper.

WriteSAE: Sparse Autoencoders for Recurrent State Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 92

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WriteSAE: Sparse Autoencoders for Recurrent State cites this paper.

WriteSAE: Sparse Autoencoders for Recurrent State Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 92

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WriteSAE: Sparse Autoencoders for Recurrent State cites this paper.

WriteSAE: Sparse Autoencoders for Recurrent State Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Chessformer: A Unified Architecture for Chess Modeling cites this paper.

Chessformer: A Unified Architecture for Chess Modeling Finding Neurons in a Haystack: Case Studies with Sparse Probing

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Mechanistic Interpretability for Learning Assurance of a Vision-Based Landing System cites this paper.

Mechanistic Interpretability for Learning Assurance of a Vision-Based Landing System Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T06:59:45.613970Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 07d64787-a15f-438a-89ff-1914881b1c47 · inbound

Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection cites this paper.

Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T14:03:29.899390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3be4657e-7245-4189-9fc4-c416c43d5ea1 · inbound

A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders cites this paper.

A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:47:09.880393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3c7b6049-ae6d-4c68-acde-9e33d0c80609 · inbound

The Amplifying Mirror: Locating and Steering the Partisan Direction inside a Large Language Model cites this paper.

The Amplifying Mirror: Locating and Steering the Partisan Direction inside a Large Language Model Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:07:26.683370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 00bcaa89-c0dd-4856-9b0d-2f8e0d13231e · inbound

ICA Lens: Interpreting Language Models Without Training Another Dictionary cites this paper.

ICA Lens: Interpreting Language Models Without Training Another Dictionary Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T09:17:49.080628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 43bd97e9-8bd9-4a19-a489-39a40bc52ad4 · inbound

From Texts to Scores: Tracing the Emergence of Essay Quality Representations in Large Language Models cites this paper.

From Texts to Scores: Tracing the Emergence of Essay Quality Representations in Large Language Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:59:34.280364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8721b206-13da-4095-9e91-3c029648a611 · inbound

Critical Percolation as a Synthetic Data Model for Interpretability cites this paper.

Critical Percolation as a Synthetic Data Model for Interpretability Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T03:49:29.699180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 862c9084-0db2-4bce-ad60-137bcc7398ed · inbound

Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models cites this paper.

Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 256

Resolution: verified exact; arxiv_id, observed 2026-07-04T06:39:37.875428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 82de02b5-f4c0-4f02-972a-06cae982d7ee · inbound

Individual Parameters in Weight-Sparse Transformers Appear Interpretable cites this paper.

Individual Parameters in Weight-Sparse Transformers Appear Interpretable Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-12T05:48:11.148130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5b2eb1b-1fb1-44ad-950b-e4e237767793 · inbound

Mechanistic Interpretability for Neural Networks: Circuits, Sparse Features and Symbolic Reasoning cites this paper.

Mechanistic Interpretability for Neural Networks: Circuits, Sparse Features and Symbolic Reasoning Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 34

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T15:06:18.076196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a618b0e9-6ab8-47c6-96d7-5ce7574e489d · inbound

How are linear representations learned? Exact solutions to the dynamics of abstraction cites this paper.

How are linear representations learned? Exact solutions to the dynamics of abstraction Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-13T06:19:30.027337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d26fe9ab-a94d-4528-a4ed-9bb07f16a09d · inbound

U-Lens: Supporting User Uncertainty Management in Long-Form LLM Responses cites this paper.

U-Lens: Supporting User Uncertainty Management in Long-Form LLM Responses Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-14T10:32:54.448282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3e6d4cc-123d-46cc-857f-af84d30a0d3b · inbound

CADENCE: A Cardiac Atom Dictionary for Interpretable Neural Concept Extraction from ECG Foundation Models cites this paper.

CADENCE: A Cardiac Atom Dictionary for Interpretable Neural Concept Extraction from ECG Foundation Models Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T03:03:56.744673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers