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A common goal of mechanistic interpretability is to decompose the activations of neural networks into features: interpretable properties of the input computed by the model.
Sparse coding with an overcomplete basis set: A strategy employed by v1?
Bruno A Olshausen and David J Field · 1997
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Sparse deep belief net model for visual area v2
Honglak Lee, Chaitanya Ekanadham, and Andrew Ng · 2007
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Alireza Makhzani and Brendan Frey · 2014
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Understanding image representations by measuring their equivariance and equivalence
Karel Lenc and Andrea Vedaldi · 2015
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Zoom in: An introduction to circuits
Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter · 2020
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Revisiting model stitching to compare neural representations
Yamini Bansal, Preetum Nakkiran, and Boaz Barak · 2021
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A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah · 2021
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Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, et al · 2022
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Emergent world representations: Exploring a sequence model trained on a synthetic task
Kenneth Li, Aspen K Hopkins, David Bau, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg · 2022
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Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 2022
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In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al · 2022
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Interpretability in the wild: a circuit for indirect object identification in gpt-2 small
Kevin Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt · 2022
Cited alongside, same era.
Language models can explain neurons in language models
Steven Bills, Nick Cammarata, Dan Mossing, Henk Tillman, Leo Gao, Gabriel Goh, Ilya Sutskever, Jan Leike, Jeff Wu, and William Saunders · 2023
Cited alongside, same era.
Towards monosemanticity: Decomposing language models with dictionary learning
Trenton Bricken, Adly Templeton, Joshua Batson, Brian Chen, Adam Jermyn, Tom Conerly, Nick Turner, Cem Anil, Carson Denison, Amanda Askell, et al · 2023
Cited alongside, same era.
Sparse autoencoders find highly interpretable features in language models
Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey · 2023
Cited alongside, same era.
Dissecting recall of factual associations in auto-regressive language models
Transcoders find interpretable llm feature circuits
Jacob Dunefsky, Philippe Chlenski, and Neel Nanda · 2024
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Not all language model features are linear
Joshua Engels, Isaac Liao, Eric J Michaud, Wes Gurnee, and Max Tegmark · 2024
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Scaling and evaluating sparse autoencoders
Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu · 2024
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Interpreting attention layer outputs with sparse autoencoders
Connor Kissane, Robert Krzyzanowski, Joseph Isaac Bloom, Arthur Conmy, and Neel Nanda · 2024
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Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2, 2024
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Mor Geva, Jasmijn Bastings, Katja Filippova, and Amir Globerson · 2023
Cited alongside, same era.
Language models represent space and time
Wes Gurnee and Max Tegmark · 2023
Cited alongside, same era.
Neuronpedia: Interactive reference and tooling for analyzing neural networks, 2023
Johnny Lin · 2023
Cited alongside, same era.
Progress measures for grokking via mechanistic interpretability
Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, and Jacob Steinhardt · 2023
Cited alongside, same era.
Sparse autoencoders find composed features in small toy models
Evan Anders, Clement Neo, Jason Hoelscher-Obermaier, and Jessica N. Howard · 2024
Cited alongside, same era.
Evaluating sparse autoencoders on concept removal tasks, 2024
Anonymous · 2024
Cited alongside, same era.
Bart Bussmann, Patrick Leask, and Neel Nanda · 2024
Cited alongside, same era.
Improving dictionary learning with gated sparse autoencoders
Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Tom Lieberum, Vikrant Varma, János Kramár, Rohin Shah, and Neel Nanda
Cited in the paper.
Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, János Kramár, Anca Dragan, Rohin Shah, and Neel Nanda · 2024
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Towards principled evaluations of sparse autoencoders for interpretability and control
Aleksandar Makelov, George Lange, and Neel Nanda · 2024
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Sparse feature circuits: Discovering and editing interpretable causal graphs in language models
Samuel Marks, Can Rager, Eric J Michaud, Yonatan Belinkov, David Bau, and Aaron Mueller · 2024
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The ‘strong’ feature hypothesis could be wrong
Lewis Smith · 2024
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Gemma 2: Improving open language models at a practical size
Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al · 2024
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Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet
Adly Templeton · 2024
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Relational composition in neural networks: A survey and call to action
Martin Wattenberg and Fernanda B Viégas · 2024
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