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A popular new method in mechanistic interpretability is to train high-dimensional sparse autoencoders (SAEs) on neuron activations and use SAE features as the atomic units of analysis.
On the proper treatment of connectionism
Paul Smolensky. 1988 · 1988
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Pytorch: An imperative style, high-performance deep learning library
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Language models are unsupervised multitask learners
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Thread: Circuits
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How do decisions emerge across layers in neural models? interpretation with differentiable masking
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Neural natural language inference models partially embed theories of lexical entailment and negation
Atticus Geiger, Kyle Richardson, and Christopher Potts. 2020 · 2020
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Zoom in: An introduction to circuits
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Investigating gender bias in language models using causal mediation analysis
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart M. Shieber. 2020 · 2020
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An interpretability illusion for bert
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Are neural nets modular? inspecting functional modularity through differentiable weight masks
Róbert Csordás, Sjoerd van Steenkiste, and Jürgen Schmidhuber. 2021 · 2021
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Causal analysis of syntactic agreement mechanisms in neural language models
Matthew Finlayson, Aaron Mueller, Sebastian Gehrmann, Stuart Shieber, Tal Linzen, and Yonatan Belinkov. 2021 · 2021
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. 2021 · 2021
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Causal abstractions of neural networks
Atticus Geiger, Hanson Lu, Thomas Icard, and Christopher Potts. 2021 · 2021
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Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2021 · 2021
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Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. 2022 · 2022
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Sparse interventions in language models with differentiable masking
Nicola De Cao, Leon Schmid, Dieuwke Hupkes, and Ivan Titov. 2022 · 2022
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Natural language descriptions of deep visual features
Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili, Antonio Torralba, and Jacob Andreas. 2022 · 2022
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Locating and editing factual associations in GPT
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 · 2022
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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 · 2023
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Towards monosemanticity: Decomposing language models with dictionary learning
Open source sparse autoencoders for all residual stream layers of gpt2 small
Joseph Bloom. 2024 · 2024
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Identifying functionally important features with end-to-end sparse dictionary learning
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Not all language model features are linear
Joshua Engels, Isaac Liao, Eric J. Michaud, Wes Gurnee, and Max Tegmark. 2024 · 2024
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Nnsight and ndif: Democratizing access to foundation model internals
Jaden Fiotto-Kaufman, Alexander R Loftus, Eric Todd, Jannik Brinkmann, Caden Juang, Koyena Pal, Can Rager, Aaron Mueller, Samuel Marks, Arnab Sen Sharma, Francesca Lucchetti, Michael Ripa, Adam Belfki, Nikhil Prakash, Sumeet Multani, Carla Brodley, Arjun Guha, Jonathan Bell, Byron Wallace, and David Bau. 2024 · 2024
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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 · 2023
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Discovering variable binding circuitry with desiderata
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Dissecting recall of factual associations in auto-regressive language models
Mor Geva, Jasmijn Bastings, Katja Filippova, and Amir Globerson. 2023 · 2023
Cited alongside, same era.
Finding neurons in a haystack: Case studies with sparse probing
Wes Gurnee, Neel Nanda, Matthew Pauly, Katherine Harvey, Dmitrii Troitskii, and Dimitris Bertsimas. 2023 · 2023
Cited alongside, same era.
Measuring and manipulating knowledge representations in language models
Evan Hernandez, Belinda Z Li, and Jacob Andreas. 2023 · 2023
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Rigorously assessing natural language explanations of neurons
Jing Huang, Atticus Geiger, Karel D’Oosterlinck, Zhengxuan Wu, and Christopher Potts. 2023 · 2023
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Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu. 2024 · 2024
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Finding alignments between interpretable causal variables and distributed neural representations
Atticus Geiger, Zhengxuan Wu, Christopher Potts, Thomas Icard, and Noah D. Goodman. 2024b · 2024
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Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2
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Towards principled evaluations of sparse autoencoders for interpretability and control
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Sparse feature circuits: Discovering and editing interpretable causal graphs in language models
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A multimodal automated interpretability agent
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Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet
Adly Templeton, Tom Conerly, Jonathan Marcus, Jack Lindsey, Trenton Bricken, Brian Chen, Adam Pearce, Craig Citro, Emmanuel Ameisen, Andy Jones, Hoagy Cunningham, Nicholas L Turner, Callum McDougall, Monte MacDiarmid, C. Daniel Freeman, Theodore R. Sumers, Edward Rees, Joshua Batson, Adam Jermyn, Shan Carter, Chris Olah, and Tom Henighan. 2024 · 2024
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