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Transformer language models (LMs) have been shown to represent concepts as directions in the latent space of hidden activations.
Learning distributed representations of concepts using linear relational embedding
Alberto Paccanaro and Geoffrey E. Hinton. 2001 · 2001
Earlier work this paper cites.
Understanding neural networks through deep visualization
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson. 2015 · 2015
Earlier work this paper cites.
Probing for semantic evidence of composition by means of simple classification tasks
Allyson Ettinger, Ahmed Elgohary, and Philip Resnik. 2016 · 2016
Earlier work this paper cites.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al. 2018 · 2018
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2021 · 2021
Cited alongside, same era.
Multimodal neurons in artificial neural networks
Gabriel Goh, Nick Cammarata, Chelsea Voss, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, and Chris Olah. 2021 · 2021
Cited alongside, same era.
Implicit representations of meaning in neural language models
Belinda Z. Li, Maxwell Nye, and Jacob Andreas. 2021 · 2021
Cited alongside, same era.
Gpt-j-6b: A 6 billion parameter autoregressive language model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
Cited alongside, same era.
Probing classifiers: Promises, shortcomings, and advances
Yonatan Belinkov. 2022 · 2022
Cited alongside, same era.
Measuring and manipulating knowledge representations in language models
Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, et al. 2022 · 2022
Later among the works it cites.
Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 · 2022
Later among the works it cites.
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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Dissecting recall of factual associations in auto-regressive language models
Mor Geva, Jasmijn Bastings, Katja Filippova, and Amir Globerson. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Evan Hernandez, Belinda Z Li, and Jacob Andreas. 2023a
Cited in the paper.
Linearity of relation decoding in transformer language models
Evan Hernandez, Arnab Sen Sharma, Tal Haklay, Kevin Meng, Martin Wattenberg, Jacob Andreas, Yonatan Belinkov, and David Bau. 2023b
Cited in the paper.
Closest in time.