Fetching the paper…
Reading the bibliography…
Neurons in auto-regressive language models like GPT-2 can be interpreted by analyzing their activation patterns.
Dictionary learning algorithms for sparse representation
Kenneth Kreutz-Delgado, Joseph F Murray, Bhaskar D Rao, Kjersti Engan, Te-Won Lee, and Terrence J Sejnowski · 2003
Earlier work this paper cites.
Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression
Yi Ma, Harm Derksen, Wei Hong, and John Wright · 2007
Earlier work this paper cites.
Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
Earlier work this paper cites.
Learning Fast Approximations of Sparse Coding
Karol Gregor and Yann LeCun · 2010
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Measuring catastrophic forgetting in neural networks
Ronald Kemker, Marc McClure, Angelina Abitino, Tyler Hayes, and Christopher Kanan · 2018
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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
Earlier work this paper cites.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
Earlier work this paper cites.
Language Models are Few-shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
Earlier work this paper cites.
Stereoset: Measuring stereotypical bias in pretrained language models
Moin Nadeem, Anna Bethke, and Siva Reddy · 2020
Earlier work this paper cites.
On the linguistic representational power of neural machine translation models
Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and James Glass · 2020
Earlier work this paper cites.
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
Yaodong Yu, Kwan Ho Ryan Chan, Chong You, Chaobing Song, and Yi Ma · 2020
Earlier work this paper cites.
The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2020
Cited alongside, same era.
A survey on bias in deep nlp
Ismael Garrido-Muñoz, Arturo Montejo-Ráez, Fernando Martínez-Santiago, and L Alfonso Ureña-López · 2021
Cited alongside, same era.
Zeyu Yun, Yubei Chen, Bruno A Olshausen, and Yann LeCun · 2021
Cited alongside, same era.
Natural language descriptions of deep visual features
Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili, Antonio Torralba, and Jacob Andreas · 2021
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
My best guess at the important tricks for training 1l saes
Arthur Conmy · 2023
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
Later among the works it cites.
A toy model of universality: Reverse engineering how networks learn group operations
Bilal Chughtai, Lawrence Chan, and Neel Nanda · 2023
Later among the works it cites.
Trustworthy llms: a survey and guideline for evaluating large language models’ alignment
Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, and Hang Li · 2023
Later among the works it cites.
Weak-to-strong generalization: Eliciting strong capabilities with weak supervision
Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, et al · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Mechanistic interpretability, variables, and the importance of interpretable bases
Chris Olah · 2022
Cited alongside, same era.
Neuron-level interpretation of deep nlp models: A survey
Hassan Sajjad, Nadir Durrani, and Fahim Dalvi · 2022
Cited alongside, same era.
Interpretability in the wild: a circuit for indirect object identification in gpt-2 small
Kevin Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt · 2022
Cited alongside, same era.
Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, et al · 2022
Cited alongside, same era.
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, and Yi Ma · 2022
Cited alongside, same era.
High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications
John Wright and Yi Ma · 2022
Cited alongside, same era.
Harnessing the power of llms in practice: A survey on chatgpt and beyond
Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, and Xia Hu · 2023
Cited alongside, same era.
Later among the works it cites.
Tom Lieberum, Matthew Rahtz, János Kramár, Geoffrey Irving, Rohin Shah, and Vladimir Mikulik · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Later among the works it cites.
Automated interpretability
Jeff Wu, Hank Tillman, and Steven Bills · 2023
Later among the works it cites.
Eyes wide shut? exploring the visual shortcomings of multimodal llms
Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie · 2024
Closest in time.
Investigating the catastrophic forgetting in multimodal large language model fine-tuning
Yuexiang Zhai, Shengbang Tong, Xiao Li, Mu Cai, Qing Qu, Yong Jae Lee, and Yi Ma · 2024
Closest in time.
Attention saes scale to gpt-2 small
Connor Kissane · 2024
Closest in time.
Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet
Adly Templeton · 2024
Closest in time.
Jumping ahead: Improving reconstruction fidelity with jumprelu sparse autoencoders
Senthooran Rajamanoharan, Tom Lieberum, Nicolas Sonnerat, Arthur Conmy, Vikrant Varma, János Kramár, and Neel Nanda · 2024
Closest in time.
Towards automated circuit discovery for mechanistic interpretability
Arthur Conmy, Augustine Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adrià Garriga-Alonso · 2024
Closest in time.
Scaling white-box transformers for vision
Jinrui Yang, Xianhang Li, Druv Pai, Yuyin Zhou, Yi Ma, Yaodong Yu, and Cihang Xie · 2024
Closest in time.