Fetching the paper…
Reading the bibliography…
Fine-grained steering of language model outputs is essential for safety and reliability.
Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake VanderPlas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Edouard Duchesnay · 2011
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomás Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Linguistic regularities in continuous space word representations
Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig · 2013
Earlier work this paper cites.
GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
Earlier work this paper cites.
Man is to computer programmer as woman is to homemaker? Debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, and Adam T. Kalai · 2016
Earlier work this paper cites.
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
Earlier work this paper cites.
Tom White · 2016
Earlier work this paper cites.
What do neural machine translation models learn about morphology?
Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and James Glass · 2017
Earlier work this paper cites.
Decoding the thought vector, 2017
Gabriel Goh · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Earlier work this paper cites.
Deep feature interpolation for image content changes
Paul Upchurch, Jacob R. Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, and Kilian Q. Weinberger · 2017
Earlier work this paper cites.
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, German Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni · 2018
Earlier work this paper cites.
Under the hood: Using diagnostic classifiers to investigate and improve how language models track agreement information
Mario Giulianelli, Jack Harding, Florian Mohnert, Dieuwke Hupkes, and Willem Zuidema · 2018
Earlier work this paper cites.
A structural probe for finding syntax in word representations
John Hewitt and Christopher D. Manning · 2019
Earlier work this paper cites.
AllenNLP interpret: A framework for explaining predictions of NLP models
Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian, Matt Gardner, and Sameer Singh · 2019
Earlier work this paper cites.
Implicit semantic data augmentation for deep networks
Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Gao Huang, and Cheng Wu · 2019
Earlier work this paper cites.
Investigating gender bias in language models using causal mediation analysis
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart Shieber · 2020
Earlier work this paper cites.
Causal abstractions of neural networks
Atticus Geiger, Hanson Lu, Thomas Icard, and Christopher Potts · 2021
Earlier work this paper cites.
Causal scrubbing: A method for rigorously testing interpretability hypotheses
Lawrence Chan, Adrià Garriga-Alonso, Nicholas Goldowsky-Dill, Ryan Greenblatt, Jenny Nitishinskaya, Ansh Radhakrishnan, Buck Shlegeris, and Nate Thomas · 2022
Earlier work this paper cites.
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
Earlier work this paper cites.
Inducing causal structure for interpretable neural networks
Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thomas Icard, Noah D. Goodman, and Christopher Potts · 2022
Earlier work this paper cites.
LoRA: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
Cited alongside, same era.
Locating and editing factual associations in GPT
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 2022
Cited alongside, same era.
Extracting latent steering vectors from pretrained language models
Nishant Subramani, Nivedita Suresh, and Matthew Peters · 2022
Cited alongside, same era.
Language models can explain neurons in language models, 2023
Steven Bills, Nick Cammarata, Dan Mossing, Henk Tillman, Leo Gao, Gabriel Goh, Ilya Sutskever, Jan Leike, Jeff Wu, and William Saunders · 2023
Cited alongside, same era.
Localizing model behavior with path patching
Nicholas Goldowsky-Dill, Chris MacLeod, Lucas Sato, and Aryaman Arora · 2023
Cited alongside, same era.
Sparse autoencoders find highly interpretable features in language models
Robert Huben, Hoagy Cunningham, Logan Riggs, Aidan Ewart, and Lee Sharkey · 2024
Later among the works it cites.
Open source automated interpretability for sparse autoencoder features, 2024
Caden Juang, Gonçalo Paulo, Jacob Drori, and Nora Belrose · 2024
Later among the works it cites.
Sieve: SAEs beat baselines on a real-world task (a code generation case study)
Adam Karvonen, Dhruv Pai, Mason Wang, and Ben Keigwin · 2024
Later among the works it cites.
Sparse autoencoders reveal universal feature spaces across large language models
Michael Lan, Philip Torr, Austin Meek, Ashkan Khakzar, David Krueger, and Fazl Barez · 2024
Later among the works it cites.
Inference-time intervention: Eliciting truthful answers from a language model
Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
AlpacaEval: An automatic evaluator of instruction-following models
Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
Cited alongside, same era.
Understanding and controlling a maze-solving policy network
Ulisse Mini, Peli Grietzer, Mrinank Sharma, Austin Meek, Monte MacDiarmid, and Alexander Matt Turner · 2023
Cited alongside, same era.
Emergent linear representations in world models of self-supervised sequence models
Neel Nanda, Andrew Lee, and Martin Wattenberg · 2023
Cited alongside, same era.
The linear representation hypothesis and the geometry of large language models
Kiho Park, Yo Joong Choe, and Victor Veitch · 2023
Cited alongside, same era.
Interpretability in the wild: a circuit for indirect object identification in GPT-2 small
Kevin Ro Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt · 2023
Cited alongside, same era.
Representation engineering: A top-down approach to AI transparency
Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart, Sanmi Koyejo, Dawn Song, Matt Fredrikson, J. Zico Kolter, and Dan Hendrycks · 2023
Cited alongside, same era.
Foundational challenges in assuring alignment and safety of large language models
Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, Benjamin L. Edelman, Zhaowei Zhang, Mario Günther, Anton Korinek, Jose Hernandez-Orallo, Lewis Hammond, Eric Bigelow, Alexander Pan, Lauro Langosco, Tomasz Korbak, Heidi Zhang, Ruiqi Zhong, Seán Ó hÉigeartaigh, Gabriel Recchia, Giulio Corsi, Alan Chan, Markus Anderljung, Lilian Edwards, Aleksandar Petrov, Christian Schroeder de Witt, Sumeet Ramesh Motwan, Yoshua Bengio, Danqi Chen, Philip H. S. Torr, Samuel Albanie, Tegan Maharaj, Jakob Foerster, Florian Tramer, He He, Atoosa Kasirzadeh, Yejin Choi, and David Krueger · 2024
Cited alongside, same era.
Later among the works it cites.
Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2
Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, Janos Kramar, Anca Dragan, Rohin Shah, and Neel Nanda · 2024
Later among the works it cites.
In-context vectors: Making in context learning more effective and controllable through latent space steering
Sheng Liu, Haotian Ye, Lei Xing, and James Y. Zou · 2024
Later among the works it cites.
Sparse autoencoders match supervised features for model steering on the IOI task
Aleksandar Makelov · 2024
Later among the works it cites.
Samuel Marks and Max Tegmark · 2024
Later among the works it cites.
Can sparse autoencoders be used to decompose and interpret steering vectors?
Harry Mayne, Yushi Yang, and Adam Mahdi · 2024
Later among the works it cites.
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
Later among the works it cites.
Towards reliable evaluation of behavior steering interventions in LLMs, 2024
Itamar Pres, Laura Ruis, Ekdeep Singh Lubana, and David Krueger · 2024
Later among the works it cites.
Steering Llama 2 via contrastive activation addition
Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Turner · 2024
Later among the works it cites.
Mechanistic?
Naomi Saphra and Sarah Wiegreffe · 2024
Later among the works it cites.
Scaling monosemanticity: Extracting interpretable features from Claude 3 Sonnet
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
Later among the works it cites.
Steering language models with activation engineering
Alexander Matt Turner, Lisa Thiergart, Gavin Leech, David Udell, Juan J. Vazquez, Ulisse Mini, and Monte MacDiarmid · 2024
Later among the works it cites.
Extending activation steering to broad skills and multiple behaviours
Teun van der Weij, Massimo Poesio, and Nandi Schoots · 2024
Later among the works it cites.
pyvene: A library for understanding and improving PyTorch models via interventions
Zhengxuan Wu, Atticus Geiger, Aryaman Arora, Jing Huang, Zheng Wang, Noah Goodman, Christopher Manning, and Christopher Potts · 2024
Later among the works it cites.
Towards unifying interpretability and control: Evaluation via intervention
Usha Bhalla, Suraj Srinivas, Asma Ghandeharioun, and Himabindu Lakkaraju · 2025
Closest in time.
Speech and Language Processing
Dan Jurafsky and James H. Martin · 2025
Closest in time.
Improving alignment and robustness with circuit breakers, 2024
Andy Zou, Long Phan, Justin Wang, Derek Duenas, Maxwell Lin, Maksym Andriushchenko, Rowan Wang, Zico Kolter, Matt Fredrikson, and Dan Hendrycks · 2025
Closest in time.