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Although it is known that transformer language models (LMs) pass features from early layers to later layers, it is not well understood how this information is represented and routed by the model.
Distributed representations, simple recurrent networks, and grammatical structure
Jeffrey L. Elman · 1991
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Matching pursuits with time-frequency dictionaries
Stéphane G Mallat and Zhifeng Zhang · 1993
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Compositional Explanations of Neurons, June 2020
Jesse Mu and Jacob Andreas · 2006
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Building machines that learn and think like people
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
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Openwebtext corpus
Aaron Gokaslan and Vanya Cohen · 2019
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Zoom in: An introduction to circuits
Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter · 2020
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Intrinsic dimensionality explains the effectiveness of language model fine-tuning
Armen Aghajanyan, Sonal Gupta, and Luke Zettlemoyer · 2021
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A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah · 2021
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Compacter: Efficient low-rank hypercomplex adapter layers
Rabeeh Karimi Mahabadi, James Henderson, and Sebastian Ruder · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh · 2021
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The Singular Value Decompositions of Transformer Weight Matrices are Highly Interpretable
beren and Sid Black · 2022
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Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei · 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, Roger Grosse, Sam McCandlish, Jared Kaplan, Dario Amodei, Martin Wattenberg, and Christopher Olah · 2022
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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
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp · 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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The SVD of Convolutional Weights: A CNN Interpretability Framework, August 2022
Brenda Praggastis, Davis Brown, Carlos Ortiz Marrero, Emilie Purvine, Madelyn Shapiro, and Bei Wang · 2022
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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 · 2022
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Do prompt-based models really understand the meaning of their prompts?
Albert Webson and Ellie Pavlick · 2022
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Opt: Open pre-trained transformer language models, 2022
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer · 2022
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Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al · 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, 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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What algorithms can transformers learn? a study in length generalization
Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin, Omid Saremi, Joshua Susskind, Samy Bengio, and Preetum Nakkiran · 2023
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Lora-xs: Low-rank adaptation with extremely small number of parameters
Klaudia Bałazy, Mohammadreza Banaei, Karl Aberer, and Jacek Tabor · 2024
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Trilora: Integrating svd for advanced style personalization in text-to-image generation
Chengcheng Feng, Mu He, Qiuyu Tian, Haojie Yin, Xiaofang Zhao, Hongwei Tang, and Xingqiang Wei · 2024
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Lq-lora: Low-rank plus quantized matrix decomposition for efficient language model finetuning
Han Guo, Philip Greengard, Eric Xing, and Yoon Kim · 2024
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Anchored Answers: Unravelling Positional Bias in GPT-2’s Multiple-Choice Questions, May 2024
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Sparse autoencoders find highly interpretable features in language models, 2023
Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey · 2023
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How do Language Models Bind Entities in Context?, October 2023
Jiahai Feng and Jacob Steinhardt · 2023
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Causal abstraction for faithful model interpretation
Atticus Geiger, Chris Potts, and Thomas Icard · 2023
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Localizing model behavior with path patching
Nicholas Goldowsky-Dill, Chris MacLeod, Lucas Sato, and Aryaman Arora · 2023
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Finding Neurons in a Haystack: Case Studies with Sparse Probing, June 2023
Wes Gurnee, Neel Nanda, Matthew Pauly, Katherine Harvey, Dmitrii Troitskii, and Dimitris Bertsimas · 2023
Cited alongside, same era.
How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model
Michael Hanna, Ollie Liu, and Alexandre Variengien · 2023
Cited alongside, same era.
Break it down: Evidence for structural compositionality in neural networks
Michael Lepori, Thomas Serre, and Ellie Pavlick · 2023
Cited alongside, same era.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
Cited alongside, same era.
Ruizhe Li and Yanjun Gao · 2024
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Lost in the middle: How language models use long contexts
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang · 2024
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Is this the subspace you are looking for? an interpretability illusion for subspace activation patching
Aleksandar Makelov, Georg Lange, Atticus Geiger, and Neel Nanda · 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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Circuit component reuse across tasks in transformer language models
Jack Merullo, Carsten Eickhoff, and Ellie Pavlick · 2024
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Understanding addition in transformers
Philip Quirke and Fazl Barez · 2024
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Improving dictionary learning with gated sparse autoencoders, 2024
Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Tom Lieberum, Vikrant Varma, János Kramár, Rohin Shah, and Neel Nanda · 2024
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Aaditya K Singh, Ted Moskovitz, Felix Hill, Stephanie CY Chan, and Andrew M Saxe · 2024
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Svfit: Parameter-efficient fine-tuning of large pre-trained models using singular values
Chengwei Sun, Jiwei Wei, Yujia Wu, Yiming Shi, Shiyuan He, Zeyu Ma, Ning Xie, and Yang Yang · 2024
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Language-specific neurons: The key to multilingual capabilities in large language models
Tianyi Tang, Wenyang Luo, Haoyang Huang, Dongdong Zhang, Xiaolei Wang, Xin Zhao, Furu Wei, and Ji-Rong Wen · 2024
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Svd-llm: Truncation-aware singular value decomposition for large language model compression
Xin Wang, Yu Zheng, Zhongwei Wan, and Mi Zhang · 2024
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Don’t listen to me: Understanding and exploring jailbreak prompts of large language models
Zhiyuan Yu, Xiaogeng Liu, Shunning Liang, Zach Cameron, Chaowei Xiao, and Ning Zhang · 2024
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Instilling inductive biases with subnetworks, 2024
Enyan Zhang, Michael A. Lepori, and Ellie Pavlick · 2024
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Large Language Models Are Not Robust Multiple Choice Selectors, February 2024
Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, and Minlie Huang · 2024
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Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data
Charles H. Martin, Tongsu (Serena) Peng, and Michael W. Mahoney · 2041
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