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A key challenge in AI alignment is guiding large language models (LLMs) to follow desired behaviors at test time.
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Bruno A Olshausen and David J Field · 1996
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Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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Sparse deep belief net model for visual area v2
Honglak Lee, Chaitanya Ekanadham, and Andrew Ng · 2007
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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k-sparse autoencoders
Alireza Makhzani and Brendan J. Frey · 2013
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Language modeling with gated convolutional networks
Yann N Dauphin, Angela Fan, Michael Auli, and David Grangier · 2017
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Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, D. Song, and J. Steinhardt · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Glu variants improve transformer
Noam Shazeer · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre · 2022
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie C. Lin, Jacob Hilton, and Owain Evans · 2022
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Extracting latent steering vectors from pretrained language models
Nishant Subramani, Nivedita Suresh, and Matthew E Peters · 2022
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Towards monosemanticity: Decomposing language models with dictionary learning
Trenton Bricken, Adly Templeton, Joshua Batson, Brian Chen, Adam Jermyn, Tom Conerly, Nick Turner, Cem Anil, Carson Denison, Amanda Askell, Robert Lasenby, Yifan Wu, Shauna Kravec, Nicholas Schiefer, Tim Maxwell, Nicholas Joseph, Zac Hatfield-Dodds, Alex Tamkin, Karina Nguyen, Brayden McLean, Josiah E Burke, Tristan Hume, Shan Carter, Tom Henighan, and Christopher Olah · 2023
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Learn to refuse: Making large language models more controllable and reliable through knowledge scope limitation and refusal mechanism
Lang Cao · 2023
Cited alongside, same era.
Sparse autoencoders find highly interpretable features in language models
Hoagy Cunningham, Aidan Ewart, Logan Riggs, R. Huben, and Lee Sharkey · 2023
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Feedback-guided data synthesis for imbalanced classification
Reyhane Askari Hemmat, Mohammad Pezeshki, Florian Bordes, Michal Drozdzal, and Adriana Romero-Soriano · 2023
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Neuronpedia: Interactive reference and tooling for analyzing neural networks, 2023
Johnny Lin · 2023
Cited alongside, same era.
Steering llama 2 via contrastive activation addition
Nina Panickssery, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Matt Turner · 2023
Controllable text generation for large language models: A survey
Xun Liang, Hanyu Wang, Yezhaohui Wang, Shichao Song, Jiawei Yang, Simin Niu, Jie Hu, Dan Liu, Shunyu Yao, Feiyu Xiong, and Zhiyu Li · 2024
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Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2
Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, J’anos Kram’ar, Anca Dragan, Rohin Shah, and Neel Nanda · 2024
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Can sparse autoencoders be used to decompose and interpret steering vectors?
Harry Mayne, Yushi Yang, and Adam Mahdi · 2024
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Steering language model refusal with sparse autoencoders
Kyle O’Brien, David Majercak, Xavier Fernandes, Richard Edgar, Jingya Chen, Harsha Nori, Dean Carignan, Eric Horvitz, and Forough Poursabzi-Sangde · 2024
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Gpt-4o system card
OpenAI, :, Aaron Hurst, Adam Lerer, Adam P. Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al · 2024
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Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, E. Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn · 2023
Cited alongside, same era.
Activation addition: Steering language models without optimization
Alexander Matt Turner, Lisa Thiergart, Gavin Leech, David Udell, Juan J Vazquez, Ulisse Mini, and Monte MacDiarmid · 2023
Cited alongside, same era.
Instruction tuning for large language models: A survey
Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, and Guoyin Wang · 2023
Cited alongside, same era.
Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2023
Cited alongside, same era.
Towards inference-time category-wise safety steering for large language models
Amrita Bhattacharjee, Shaona Ghosh, Traian Rebedea, and Christopher Parisien · 2024
Cited alongside, same era.
Personalized steering of large language models: Versatile steering vectors through bi-directional preference optimization
Yuanpu Cao, Tianrong Zhang, Bochuan Cao, Ziyi Yin, Lu Lin, Fenglong Ma, and Jinghui Chen · 2024
Cited alongside, same era.
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 · 2024
Cited alongside, same era.
Nate Rahn, Pierluca D’Oro, and Marc G Bellemare · 2024
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A systematic survey of prompt engineering in large language models: Techniques and applications
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha · 2024
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Sae features for refusal and sycophancy steering vectors
Stepan Shabalin, Dmitrii Kharlapenko, Arthur Conmy, and Neel Nanda · 2024
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Improving instruction-following in language models through activation steering
Alessandro Stolfo, Vidhisha Balachandran, Safoora Yousefi, Eric Horvitz, and Besmira Nushi · 2024
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Confabulation: The surprising value of large language model hallucinations
Peiqi Sui, Eamon Duede, Sophie Wu, and Richard Jean So · 2024
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Analyzing the generalization and reliability of steering vectors
Daniel Tan, David Chanin, Aengus Lynch, Dimitrios Kanoulas, Brooks Paige, Adria Garriga-Alonso, and Robert Kirk · 2024
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Gemma 2: Improving open language models at a practical size
Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, et al · 2024
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Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet, 2024
Adly Templeton, Tom Conerly, Jonathan Marcus, Jack Lindsey, Trenton Bricken, Brian Chen, Adam Pearce, Craig Citro, Emmanuel Ameisen, Andy Jones, Hoagy Cunningham, Nicholas L Turner, Callum McDougall, Monte MacDiarmid, C. Daniel Freeman, Theodore R. Sumers, Edward Rees, Joshua Batson, Adam Jermyn, Shan Carter, Chris Olah, and Tom Henighan · 2024
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Language models learn to mislead humans via rlhf
Jiaxin Wen, Ruiqi Zhong, Akbir Khan, Ethan Perez, Jacob Steinhardt, Minlie Huang, Samuel R. Bowman, He He, and Shi Feng · 2024
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Hallucination is inevitable: An innate limitation of large language models
Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli · 2024
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Adaptable logical control for large language models
Honghua Zhang, Po-Nien Kung, Masahiro Yoshida, Guy Van den Broeck, and Nanyun Peng · 2024
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Steering knowledge selection behaviours in llms via sae-based representation engineering
Yu Zhao, Alessio Devoto, Giwon Hong, Xiaotang Du, Aryo Pradipta Gema, Hongru Wang, Xuanli He, Kam-Fai Wong, and Pasquale Minervini · 2024
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Shared imagination: Llms hallucinate alike
Yilun Zhou, Caiming Xiong, Silvio Savarese, and Chien-Sheng Wu · 2024
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