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While induction is considered a key mechanism for in-context learning in LLMs, understanding its precise circuit decomposition beyond toy models remains elusive.
Sarthak Jain and Byron C. Wallace · 1902
Earlier work this paper cites.
Attention interpretability across nlp tasks, 2019
Shikhar Vashishth, Shyam Upadhyay, Gaurav Singh Tomar, and Manaal Faruqui · 1909
Earlier work this paper cites.
Scale invariance and universality: organizing principles in complex systems
H.E Stanley, L.A.N Amaral, P Gopikrishnan, P.Ch Ivanov, T.H Keitt, and V Plerou · 2000
Earlier work this paper cites.
Visual interrogation of attention-based models for natural language inference and machine comprehension
Shusen Liu, Tao Li, Zhimin Li, Vivek Srikumar, Valerio Pascucci, and Peer-Timo Bremer · 2007
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate, 2016
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2016
Earlier work this paper cites.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Explaining nonlinear classification decisions with deep taylor decomposition, 2017
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2017
Earlier work this paper cites.
What does bert look at? an analysis of bert’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning · 2019
Earlier work this paper cites.
Seq2seq-vis: A visual debugging tool for sequence-to-sequence models, 2019
Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, and Alexander M. Rush · 2019
Earlier work this paper cites.
A multiscale visualization of attention in the transformer model
Jesse Vig · 2019
Earlier work this paper cites.
Is attention interpretable?
Sofia Serrano and Noah A. Smith · 2019
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter · 2019
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Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners, 2019
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Earlier work this paper cites.
Quantifying attention flow in transformers
Samira Abnar and Willem Zuidema · 2020
Earlier work this paper cites.
Self-attention attribution: Interpreting information interactions inside transformer
Yaru Hao, Li Dong, Furu Wei, and Ke Xu · 2020
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Transformer interpretability beyond attention visualization
Hila Chefer, Shir Gur, and Lior Wolf · 2020
Earlier work this paper cites.
Zoom in: An introduction to circuits
Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter · 2020
Earlier work this paper cites.
interpreting gpt: the logit lens
nostalgebraist · 2020
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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.
A mathematical framework for transformer circuits
N Elhage, N Nanda, C Olsson, T Henighan, N Joseph, B Mann, A Askell, Y Bai, A Chen, T Conerly, et al · 2021
Earlier work this paper cites.
Interactive visualization and manipulation of attention-based neural machine translation
Jaesong Lee, Joong-Hwi Shin, and Jun-Seok Kim · 2021
Earlier work this paper cites.
A primer in bertology: What we know about how bert works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky · 2021
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 · 2021
Earlier work this paper cites.
Mechanistic interpretability, variables, and the importance of interpretable bases
Christopher Olah · 2022
Earlier work this paper cites.
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, Tom Conerly, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah · 2022
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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, et al · 2022
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Attention-based interpretability with concept transformers
Mattia Rigotti, Christoph Miksovic, Ioana Giurgiu, Thomas Gschwind, and P. Scotton · 2022
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Analyzing transformers in embedding space
Guy Dar, Mor Geva, Ankit Gupta, and Jonathan Berant · 2022
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Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 2022
OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, Red Avila, et al · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context, 2024
Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al · 2024
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The llama 3 herd of models, 2024
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, et al · 2024
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Circuits updates - january 2024: Further investigation of attention superposition
Adam Jermyn, Chris Olah, and Conerly Tom · 2024
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Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet
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Transformerlens, 2022
Neel Nanda and Joseph Bloom · 2022
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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 · 2023
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Progress measures for grokking via mechanistic interpretability
Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, and Jacob Steinhardt · 2023
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Towards automated circuit discovery for mechanistic interpretability
Arthur Conmy, Augustine N. Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adrià Garriga-Alonso · 2023
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Tom Lieberum, Matthew Rahtz, János Kramár, Neel Nanda, Geoffrey Irving, Rohin Shah, and Vladimir Mikulik · 2023
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Towards monosemanticity: Decomposing language models with dictionary learning
Trenton Bricken, Adly Templeton, Joshua Batson, Brian Chen, Adam Jermyn, Tom Conerly, Nicholas L. Turner, Cem Anil, Carson Denison, Amanda Askell, Robert Lasenby, Yifan Wu, Shauna Kravec, Nicholas Schiefer, Tim Maxwell, Nicholas Joseph, Alex Tamkin, Karina Nguyen, Brayden McLean, Josiah E. Burke, Tristan Hume, Shan Carter, Tom Henighan, and Chris Olah · 2023
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Successor heads: Recurring, interpretable attention heads in the wild
Rhys Gould, Euan Ong, George Ogden, and Arthur Conmy · 2023
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Adly Templeton, Tom Conerly, Jonathan Marcus, Jack Lindsey, Trenton Bricken, and Brian Chen · 2024
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Sparse autoencoders find highly interpretable features in language models
Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey · 2024
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Scaling and evaluating sparse autoencoders
Leo Gao, Tom Dupr’e la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, I. Sutskever, J. Leike, and Jeffrey Wu · 2024
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Circuits updates - july 2024: The next five hurdles
Chris Olah and Adam Jermyn · 2024
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The remarkable robustness of llms: Stages of inference?, 2024
Vedang Lad, Wes Gurnee, and Max Tegmark · 2024
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Sparse feature circuits: Discovering and editing interpretable causal graphs in language models
Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, and Aaron Mueller · 2024
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Transcoders find interpretable llm feature circuits
Jacob Dunefsky, Philippe Chlenski, and Neel Nanda · 2024
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Sparse crosscoders for cross-layer features and model diffing
Jack Lindsey, Adly Templeton, Jonathan Marcus, Thomas Conerly, Joshua Batson, and Christopher Olah · 2024
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Identifying semantic induction heads to understand in-context learning
Jie Ren, Qipeng Guo, Hang Yan, Dongrui Liu, Quanshi Zhang, Xipeng Qiu, and Dahua Lin · 2024
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Aaditya K. Singh, Ted Moskovitz, Felix Hill, Stephanie C. Y. Chan, and Andrew M. Saxe · 2024
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Unveiling induction heads: Provable training dynamics and feature learning in transformers, 2024
Siyu Chen, Heejune Sheen, Tianhao Wang, and Zhuoran Yang · 2024
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Induction heads as an essential mechanism for pattern matching in in-context learning, 2024
Joy Crosbie and Ekaterina Shutova · 2024
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Gemma 2: Improving open language models at a practical size, 2024
Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, Johan Ferret, et al · 2024
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Circuit component reuse across tasks in transformer language models
Jack Merullo, Carsten Eickhoff, and Ellie Pavlick · 2024
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Generalization from starvation: Hints of universality in llm knowledge graph learning, 2024
David D. Baek, Yuxiao Li, and Max Tegmark · 2024
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Llm circuit analyses are consistent across training and scale, 2024
Curt Tigges, Michael Hanna, Qinan Yu, and Stella Biderman · 2024
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Universal neurons in gpt2 language models
Wes Gurnee, Theo Horsley, Zifan Carl Guo, Tara Rezaei Kheirkhah, Qinyi Sun, Will Hathaway, Neel Nanda, and Dimitris Bertsimas · 2024
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Mechanistic interpretability for ai safety - a review
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A practical review of mechanistic interpretability for transformer-based language models
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Causal abstraction: A theoretical foundation for mechanistic interpretability, 2024
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Information flow routes: Automatically interpreting language models at scale
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