Z. C. Lipton. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3):31-57, 2018.
A. Lovett and K. Forbus. Modeling visual problem solving as analogical reasoning. Psychological review, 124(1):60, 2017.
A. Mahendran and A. Vedaldi. Understanding deep image representations by inverting them. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5188-5196, 2015.
K. Meng, D. Bau, A. Andonian, and Y. Belinkov. Locating and editing factual associations in GPT. Advances in Neural Information Processing Systems, 36, 2022.
T. Miller. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence, 267:1-38, 2019.
M. Mitchell. Abstraction and analogy-making in artificial intelligence. Annals of the New York Academy of Sciences, 1505(1):79-101, 2021.
J. Mu and J. Andreas. Compositional explanations of neurons. Advances in Neural Information Processing Systems, 33:17153-17163, 2020.
V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 807-814, 2010.
N. Nanda, L. Chan, T. Lieberum, J. Smith, and J. Steinhardt. Progress measures for grokking via mechanistic interpretability. In The Eleventh International Conference on Learning Representations, 2022.
T. Oikarinen and T.-W. Weng. Clip-dissect: Automatic description of neuron representations in deep vision networks. International Conference on Learning Representations, 2023.
C. Olah, A. Mordvintsev, and L. Schubert. Feature visualization. Distill, 2(11):e7, 2017.
C. Olah, N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. Zoom in: An introduction to circuits. Distill, 5(3):e00024-001, 2020.
OpenAI. Gpt-4 technical report, 2023.
V. Petsiuk, A. Das, and K. Saenko. Rise: Randomized input sampling for explanation of black-box models. In Proceedings of the British Machine Vision Conference, 2018.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618-626, 2017.
C. Singh, A. R. Hsu, R. Antonello, S. Jain, A. G. Huth, B. Yu, and J. Gao. Explaining black box text modules in natural language with language models, 2023.
D. Sorokin and I. Gurevych. Modeling semantics with gated graph neural networks for knowledge base question answering. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3306-3317, 2018.
H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, D. Bikel, L. Blecher, C. C. Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, B. Fuller, C. Gao, V. Goswami, N. Goyal, A. Hartshorn, S. Hosseini, R. Hou, H. Inan, M. Kardas, V. Kerkez, M. Khabsa, I. Kloumann, A. Korenev, P. S. Koura, M.-A. Lachaux, T. Lavril, J. Lee, D. Liskovich, Y. Lu, Y. Mao, X. Martinet, T. Mihaylov, P. Mishra, I. Molybog, Y. Nie, A. Poulton, J. Reizenstein, R. Rungta, K. Saladi, A. Schelten, R. Silva, E. M. Smith, R. Subramanian, X. E. Tan, B. Tang, R. Taylor, A. Williams, J. X. Kuan, P. Xu, Z. Yan, I. Zarov, Y. Zhang, A. Fan, M. Kambadur, S. Narang, A. Rodriguez, R. Stojnic, S. Edunov, and T. Scialom. Llama 2: Open foundation and fine-tuned chat models, 2023.
G. Van Rossum. The Python Library Reference, release 3.8.2. Python Software Foundation, 2020.