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In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience.
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Modeling stylized character expressions via deep learning
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
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Bridging the gaps between residual learning, recurrent neural networks and visual cortex
Qianli Liao and Tomaso Poggio · 2016
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The Definition of Lying and Deception
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune · 2016
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Residual networks behave like ensembles of relatively shallow networks
Andreas Veit, Michael J Wilber, and Serge Belongie · 2016
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Learning deep features for discriminative localization
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Network dissection: Quantifying interpretability of deep visual representations
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Understanding intermediate layers using linear classifier probes
Yoshua Bengio Guillaume Alain · 2017
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RACE: Large-scale ReAding comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy · 2017
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Lsdsem 2017 shared task: The story cloze test
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Learning to generate reviews and discovering sentiment
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Grad-cam: Visual explanations from deep networks via gradient-based localization
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Think you have solved question answering? try arc, the AI2 reasoning challenge
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
Ruth Fong and Andrea Vedaldi · 2018
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Residual connections encourage iterative inference
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
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Can a suit of armor conduct electricity? A new dataset for open book question answering
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Toy models of superposition
Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, Roger Grosse, Sam McCandlish, Jared Kaplan, Dario Amodei, Martin Wattenberg, and Christopher Olah · 2022
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X-risk analysis for ai research
Dan Hendrycks and Mantas Mazeika · 2022
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Membership inference attacks on machine learning: A survey
Hongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, Philip S Yu, and Xuyun Zhang · 2022
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Acquisition of chess knowledge in alphazero
Thomas McGrath, Andrei Kapishnikov, Nenad Tomašev, Adam Pearce, Martin Wattenberg, Demis Hassabis, Been Kim, Ulrich Paquet, and Vladimir Kramnik · 2022
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In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al · 2022
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2018
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Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2018
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Interpretable basis decomposition for visual explanation
Bolei Zhou, Yiyou Sun, David Bau, and Antonio Torralba · 2018
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Semantic photo manipulation with a generative image prior
David Bau, Hendrik Strobelt, William Peebles, Jonas Wulff, Bolei Zhou, Jun-Yan Zhu, and Antonio Torralba · 2019
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PIQA: reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi · 2019
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Geoepidemiological big data approach to sarcoidosis: geographical and ethnic determinants
Pilar Brito-Zerón, Belchin Kostov, Daphne Superville, Robert P Baughman, Manuel Ramos-Casals, et al · 2019
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Discovering language model behaviors with model-written evaluations, 2022
Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, Andy Jones, Anna Chen, Ben Mann, Brian Israel, Bryan Seethor, Cameron McKinnon, Christopher Olah, Da Yan, Daniela Amodei, Dario Amodei, Dawn Drain, Dustin Li, Eli Tran-Johnson, Guro Khundadze, Jackson Kernion, James Landis, Jamie Kerr, Jared Mueller, Jeeyoon Hyun, Joshua Landau, Kamal Ndousse, Landon Goldberg, Liane Lovitt, Martin Lucas, Michael Sellitto, Miranda Zhang, Neerav Kingsland, Nelson Elhage, Nicholas Joseph, Noemí Mercado, Nova DasSarma, Oliver Rausch, Robin Larson, Sam McCandlish, Scott Johnston, Shauna Kravec, Sheer El Showk, Tamera Lanham, Timothy Telleen-Lawton, Tom Brown, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Jack Clark, Samuel R. Bowman, Amanda Askell, Roger Grosse, Danny Hernandez, Deep Ganguli, Evan Hubinger, Nicholas Schiefer, and Jared Kaplan · 2022
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The internal state of an llm knows when its lying, 2023
Amos Azaria and Tom Mitchell · 2023
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Open llm leaderboard, 2023
Edward Beeching, Clémentine Fourrier, Nathan Habib, Sheon Han, Nathan Lambert, Nazneen Rajani, Omar Sanseviero, Lewis Tunstall, and Thomas Wolf · 2023
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Pythia: A suite for analyzing large language models across training and scaling, 2023
Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal · 2023
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Extracting training data from diffusion models
Nicolas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
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Existential risk from power-seeking ai
Joseph Carlsmith · 2023
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Beyond surface statistics: Scene representations in a latent diffusion model, 2023
Yida Chen, Fernanda Viégas, and Martin Wattenberg · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
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Erasing concepts from diffusion models, 2023
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, and David Bau · 2023
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Large language model ai chatbots require approval as medical devices
Stephen Gilbert, Hugh Harvey, Tom Melvin, Erik Vollebregt, and Paul Wicks · 2023
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Medalpaca – an open-source collection of medical conversational ai models and training data, 2023
Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, and Keno K. Bressem · 2023
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ehartford’s hugging face repository
Eric Hartford · 2023
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Natural selection favors ais over humans
Dan Hendrycks · 2023
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An overview of catastrophic ai risks
Dan Hendrycks, Mantas Mazeika, and Thomas Woodside · 2023
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Inspecting and editing knowledge representations in language models, 2023
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A review of opportunities and challenges of chatbots in education
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Quantifying chatgpt’s gender bias, Apr 2023
Sayash Kapoor and Arvind Narayanan · 2023
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Efficient fair pca for fair representation learning, 2023
Matthäus Kleindessner, Michele Donini, Chris Russell, and Muhammad Bilal Zafar · 2023
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Benefits, limits, and risks of gpt-4 as an ai chatbot for medicine
Peter Lee, Sebastien Bubeck, and Joseph Petro · 2023
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Tom Lieberum, Matthew Rahtz, János Kramár, Geoffrey Irving, Rohin Shah, and Vladimir Mikulik · 2023
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The hydra effect: Emergent self-repair in language model computations
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Dinov2: Learning robust visual features without supervision, 2023
Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Russell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang-Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, Gabriel Synnaeve, Ishan Misra, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2023
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Do the rewards justify the means? measuring trade-offs between rewards and ethical behavior in the machiavelli benchmark
Alexander Pan, Jun Shern Chan, Andy Zou, Nathaniel Li, Steven Basart, Thomas Woodside, Jonathan Ng, Hanlin Zhang, Scott Emmons, and Dan Hendrycks · 2023
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Kernelized concept erasure, 2023
Shauli Ravfogel, Francisco Vargas, Yoav Goldberg, and Ryan Cotterell · 2023
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Kevin Roose · 2023
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Exploring the landscape of machine unlearning: A comprehensive survey and taxonomy, 2023
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Gold doesn’t always glitter: Spectral removal of linear and nonlinear guarded attribute information, 2023
Shun Shao, Yftah Ziser, and Shay B. Cohen · 2023
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Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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There are no coherence theorems
Elliott Thornley · 2023
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Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback, 2023
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Llama 2: Open foundation and fine-tuned chat models, 2023
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom · 2023
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Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting, 2023
Miles Turpin, Julian Michael, Ethan Perez, and Samuel R. Bowman · 2023
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Coding inequity: Assessing gpt-4’s potential for perpetuating racial and gender biases in healthcare
Travis Zack, Eric Lehman, Mirac Suzgun, Jorge A. Rodriguez, Leo Anthony Celi, Judy Gichoya, Dan Jurafsky, Peter Szolovits, David W. Bates, Raja-Elie E. Abdulnour, Atul J. Butte, and Emily Alsentzer · 2023
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Mquake: Assessing knowledge editing in language models via multi-hop questions, 2023
Zexuan Zhong, Zhengxuan Wu, Christopher D. Manning, Christopher Potts, and Danqi Chen · 2023
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Universal and transferable adversarial attacks on aligned language models, 2023
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