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Interventions targeting the representation space of language models (LMs) have emerged as an effective means to influence model behavior.
RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Mathematical methods of organizing and planning production
Leonid V. Kantorovich. 1960 · 1960
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Measuring nominal scale agreement among many raters
Joseph L. Fleiss. 1971 · 1971
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Causal mediation analysis for interpreting neural NLP: The case of gender bias
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart M. Shieber. 2020 · 2004
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Accelerating scientific computations with mixed precision algorithms
Marc Baboulin, Alfredo Buttari, Jack Dongarra, Jakub Kurzak, Julie Langou, Julien Langou, Piotr Luszczek, and Stanimire Tomov. 2009 · 2009
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Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, et al. 2017 · 2017
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Bias in bios: A case study of semantic representation bias in a high-stakes setting
Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, and Adam Tauman Kalai. 2019 · 2019
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Natural questions: A benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
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It’s all in the name: Mitigating gender bias with name-based counterfactual data substitution
Rowan Hall Maudslay, Hila Gonen, Ryan Cotterell, and Simone Teufel. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Interpreting GPT: The logit lens
nostalgebraist. 2020 · 2020
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Null it out: Guarding protected attributes by iterative nullspace projection
Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, and Yoav Goldberg. 2020 · 2020
Cited alongside, same era.
Amnesic probing: Behavioral explanation with amnesic counterfactuals
Yanai Elazar, Shauli Ravfogel, Alon Jacovi, and Yoav Goldberg. 2021 · 2021
Cited alongside, same era.
CausaLM: Causal model explanation through counterfactual language models
Amir Feder, Nadav Oved, Uri Shalit, and Roi Reichart. 2021 · 2021
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Causal abstractions of neural networks
Atticus Geiger, Hanson Lu, Thomas Icard, and Christopher Potts. 2021 · 2021
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Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2021 · 2021
Cited alongside, same era.
Counterfactual interventions reveal the causal effect of relative clause representations on agreement prediction
A geometric notion of causal probing
Clément Guerner, Anej Svete, Tianyu Liu, Alexander Warstadt, and Ryan Cotterell. 2023 · 2023
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
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Inference-time intervention: Eliciting truthful answers from a language model
Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. 2023 · 2023
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Text embeddings reveal (almost) as much as text
John Morris, Volodymyr Kuleshov, Vitaly Shmatikov, and Alexander Rush. 2023 · 2023
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Log-linear guardedness and its implications
Shauli Ravfogel, Yoav Goldberg, and Ryan Cotterell. 2023 · 2023
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Shauli Ravfogel, Grusha Prasad, Tal Linzen, and Yoav Goldberg. 2021 · 2021
Cited alongside, same era.
Inducing causal structure for interpretable neural networks
Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thomas Icard, Noah Goodman, and Christopher Potts. 2022 · 2022
Cited alongside, same era.
Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 · 2022
Cited alongside, same era.
Large dual encoders are generalizable retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, and Yinfei Yang. 2022 · 2022
Cited alongside, same era.
Kernelized concept erasure
Shauli Ravfogel, Francisco Vargas, Yoav Goldberg, and Ryan Cotterell. 2022 · 2022
Cited alongside, same era.
Extracting latent steering vectors from pretrained language models
Nishant Subramani, Nivedita Suresh, and Matthew Peters. 2022 · 2022
Cited alongside, same era.
Eliciting latent predictions from transformers with the tuned lens
Nora Belrose, Zach Furman, Logan Smith, Danny Halawi, Igor Ostrovsky, Lev McKinney, Stella Biderman, and Jacob Steinhardt. 2023a
Cited in the paper.
Later among the works it cites.
On affine homotopy between language encoders
Robin S. M. Chan, Reda Boumasmoud, Anej Svete, Yuxin Ren, Qipeng Guo, Zhijing Jin, Shauli Ravfogel, Mrinmaya Sachan, Bernhard Schölkopf, Mennatallah El-Assady, et al. 2024 · 2024
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Text embedding inversion security for multilingual language models
Yiyi Chen, Heather Lent, and Johannes Bjerva. 2024 · 2024
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Patchscope: A unifying framework for inspecting hidden representations of language models
Asma Ghandeharioun, Avi Caciularu, Adam Pearce, Lucas Dixon, and Mor Geva. 2024 · 2024
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Explaining text classifiers with counterfactual representations
Pirmin Lemberger and Antoine Saillenfest. 2024 · 2024
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MiMiC: Minimally modified counterfactuals in the representation space
Shashwat Singh, Shauli Ravfogel, Jonathan Herzig, Roee Aharoni, Ryan Cotterell, and Ponnurangam Kumaraguru. 2024 · 2024
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