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Large Language Models (LLMs) have transformed natural language processing, yet their internal mechanisms remain largely opaque.
Representation degeneration problem in training natural language generation models
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
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Disentangling by factorising
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Spine: Sparse interpretable neural embeddings
Anant Subramanian, Danish Pruthi, Harsh Jhamtani, Taylor Berg-Kirkpatrick, and Eduard Hovy. 2018 · 2018
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On interpretability and feature representations: an analysis of the sentiment neuron
Jonathan Donnelly and Adam Roegiest. 2019 · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem. 2019 · 2019
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Understanding neural networks via feature visualization: A survey
Anh Nguyen, Jason Yosinski, and Jeff Clune. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Interpreting gpt: the logit lens
Nostalgebraist. 2020 · 2020
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Probing classifiers: Promises, shortcomings, and advances
Yonatan Belinkov. 2022 · 2022
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Toy models of superposition
Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, Roger Grosse, Sam McCandlish, Jared Kaplan, Dario Amodei, Martin Wattenberg, and Christopher Olah. 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
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Disentangling dense embeddings with sparse autoencoders
Charles O’Neill, Christine Ye, Kartheik Iyer, and John F Wu. 2024 · 2024
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Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey. 2023 · 2023
Cited alongside, same era.
Neuron to graph: Interpreting language model neurons at scale
Alex Foote, Neel Nanda, Esben Kran, Ioannis Konstas, Shay Cohen, and Fazl Barez. 2023 · 2023
Cited alongside, same era.
Neuronpedia: Interactive reference and tooling for analyzing neural networks
Johnny Lin. 2023 · 2023
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Distributed representations: Composition & superposition
Chris Olah. 2023 · 2023
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Introducing Claude 3.5 Sonnet
Anthropic. 2024 · 2024
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Adaptive sparse allocation with mutual choice & feature choice sparse autoencoders
Kola Ayonrinde. 2024 · 2024
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Mechanistic interpretability for ai safety–a review
Leonard Bereska and Efstratios Gavves. 2024 · 2024
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Open source sparse autoencoders for all residual stream layers of gpt2 small
Joseph Bloom. 2024 · 2024
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A practical review of mechanistic interpretability for transformer-based language models
Daking Rai, Yilun Zhou, Shi Feng, Abulhair Saparov, and Ziyu Yao. 2024 · 2024
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dictionary learning
Marks Samuel, Karvonen Adam, and Mueller Aaron. 2024 · 2024
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Naomi Saphra and Sarah Wiegreffe. 2024 · 2024
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Prolu: A nonlinearity for sparse autoencoders - ai alignment forum
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Gemma 2: Improving open language models at a practical size
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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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Addressing feature suppression in saes - ai alignment forum
Benjamin Wright and Lee Sharkey. 2024 · 2024
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Direct preference optimization using sparse feature-level constraints
Qingyu Yin, Chak Tou Leong, Hongbo Zhang, Minjun Zhu, Hanqi Yan, Qiang Zhang, Yulan He, Wenjie Li, Jun Wang, Yue Zhang, et al. 2024 · 2024
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Identifying functionally important features with end-to-end sparse dictionary learning
Dan Braun, Jordan Taylor, Nicholas Goldowsky-Dill, and Lee Sharkey. 2025 · 2025
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