References on transformer circuits, causal interventions, sparse representations, and neuroscience.
- Neel Nanda, A Comprehensive Mechanistic Interpretability Explainer & Glossary: https://www.neelnanda.io/mechanistic-interpretability/glossary
- Chris Olah et al., Zoom In: An Introduction to Circuits (Distill): https://distill.pub/2020/circuits/zoom-in/
- Bereska & Gavves, Mechanistic Interpretability for AI Safety — A Review: https://arxiv.org/abs/2404.14082
- Elhage et al., A Mathematical Framework for Transformer Circuits: https://transformer-circuits.pub/2021/framework/index.html
- TransformerLens repo + docs: https://github.com/TransformerLensOrg/TransformerLens
- TransformerLens Main Demo notebook: https://github.com/TransformerLensOrg/TransformerLens/blob/main/demos/Main_Demo.ipynb
- Olsson et al., In-context Learning and Induction Heads: https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html
- Wang et al., Interpretability in the Wild: a Circuit for IOI in GPT-2 Small: https://arxiv.org/abs/2211.00593
- TransformerLens Exploratory Analysis Demo: https://github.com/TransformerLensOrg/TransformerLens/blob/main/demos/Exploratory_Analysis_Demo.ipynb
- Elhage et al., Toy Models of Superposition: https://transformer-circuits.pub/2022/toy_model/index.html
- Olshausen & Field, Emergence of simple-cell receptive field properties by learning a sparse code: https://www.nature.com/articles/381607a0
- Rigotti et al., The importance of mixed selectivity in complex cognitive tasks: https://www.nature.com/articles/nature12160
- Bricken et al., Towards Monosemanticity: https://transformer-circuits.pub/2023/monosemantic-features/index.html
- Templeton et al., Scaling Monosemanticity: https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html
- SAELens: https://github.com/jbloomAus/SAELens
- Neuronpedia: https://www.neuronpedia.org/
- Ameisen et al., Circuit Tracing: Revealing Computational Graphs in Language Models: https://transformer-circuits.pub/2025/attribution-graphs/methods.html
- Lindsey et al., On the Biology of a Large Language Model: https://transformer-circuits.pub/2025/attribution-graphs/biology.html
- Neel Nanda, 200 Concrete Open Problems in Mechanistic Interpretability: https://www.alignmentforum.org/s/yivyHaCAmMJ3CqSyj
- Transformer Circuits thread: https://transformer-circuits.pub/
- Distill Circuits thread (vision): https://distill.pub/2020/circuits/
- Jonas & Kording, Could a Neuroscientist Understand a Microprocessor?: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005268
- Barrett, Morcos & Macke, Analyzing biological and artificial neural networks: challenges with opportunities for synergy?: https://arxiv.org/abs/1810.13373
- Cao & Yamins, Explanatory models in neuroscience: Part 1 — taking mechanistic abstraction seriously: https://arxiv.org/abs/2104.01489
- Lindsay, Convolutional Neural Networks as a Model of the Visual System: https://arxiv.org/abs/2001.07092