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Who cites «Finding Neurons in a Haystack: Case Studies with Sparse Probing»

111 papers of our library cite it (of the 149,439 whose bibliographies we read) · 30 with a page here, the most cited first · ← back to «Finding Neurons in a Haystack: Case Studies with Sparse Probing»

  1. The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False DatasetsarXiv:2310.06824Samuel Marks, Max Tegmark2023 · 169 citations
  2. Emergent Linear Representations in World Models of Self-Supervised Sequence ModelsarXiv:2309.00941Neel Nanda, Andrew Lee, Martin Wattenberg2023 · 116 citations
  3. Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2arXiv:2408.05147Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, et al.2024 · 115 citations
  4. Mechanistic Interpretability for AI Safety — A ReviewarXiv:2404.14082Leonard Bereska, Efstratios Gavves2024 · 108 citations
  5. Towards Best Practices of Activation Patching in Language Models: Metrics and MethodsarXiv:2309.16042Fred Zhang, Neel Nanda2023 · 97 citations
  6. Rethinking Interpretability in the Era of Large Language ModelsarXiv:2402.01761Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.2024 · 87 citations
  7. A Primer on the Inner Workings of Transformer-based Language ModelsarXiv:2405.00208Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.2024 · 72 citations
  8. Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse AutoencodersarXiv:2410.20526Zhengfu He, Wentao Shu, Xuyang Ge, et al.2024 · 63 citations
  9. Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in ChinchillaarXiv:2307.09458Tom Lieberum, Matthew Rahtz, Janos Kramar, et al.2023 · 58 citations
  10. Towards Controllable Natural Language Inference through Lexical Inference TypesarXiv:2308.03581Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann, et al.2023 · 2 citations

81 more papers of our library cite it; they have no page here yet.

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