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References (28)

[1]
Into the Rabbit Hull: From Task-Relevant Concepts in DINO to Minkowski Geometry
2025Thomas Fel, Binxu Wang et al.
[2]
Understanding sparse autoencoder scaling in the presence of feature manifolds
2025Eric J. Michaud, Liv Gorton et al.
[3]
Sparse but Wrong: Incorrect L0 Leads to Incorrect Features in Sparse Autoencoders
2025David Chanin, Adrià Garriga-Alonso
[4]
From Flat to Hierarchical: Extracting Sparse Representations with Matching Pursuit
2025Val'erie Costa, Thomas Fel et al.
[5]
Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs
2025Xiangchen Song, Aashiq Muhamed et al.
[6]
Feature Hedging: Correlated Features Break Narrow Sparse Autoencoders
2025David Chanin, Tom'avs Dulka et al.
[7]
Learning Multi-Level Features with Matryoshka Sparse Autoencoders
2025Bart Bussmann, Noa Nabeshima et al.
[8]
SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability
2025Adam Karvonen, Can Rager et al.
[9]
Are Sparse Autoencoders Useful? A Case Study in Sparse Probing
2025Subhash Kantamneni, Joshua Engels et al.
[10]
BatchTopK Sparse Autoencoders
2024Bart Bussmann, Patrick Leask et al.
[11]
Evaluating Sparse Autoencoders on Targeted Concept Erasure Tasks
2024Adam Karvonen, Can Rager et al.
[12]
Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders
2024Charles O'Neill, David A. Klindt
[13]
Adaptive Sparse Allocation with Mutual Choice & Feature Choice Sparse Autoencoders
2024Kola Ayonrinde
[14]
Automatically Interpreting Millions of Features in Large Language Models
2024Gonccalo Paulo, Alex Troy Mallen et al.
[15]
A is for Absorption: Studying Feature Splitting and Absorption in Sparse Autoencoders
2024David Chanin, James Wilken-Smith et al.
[16]
Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders
2024Senthooran Rajamanoharan, Tom Lieberum et al.
[17]
InterpBench: Semi-Synthetic Transformers for Evaluating Mechanistic Interpretability Techniques
2024Rohan Gupta, Iv'an Arcuschin et al.
[18]
Scaling and evaluating sparse autoencoders
2024Leo Gao, Tom Dupr'e la Tour et al.
[19]
Not All Language Model Features Are One-Dimensionally Linear
2024Joshua Engels, Isaac Liao et al.
[20]
The Linear Representation Hypothesis and the Geometry of Large Language Models
2023Kiho Park, Yo Joong Choe et al.

Showing 20 of 28 references

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