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Most currently deployed large language models (LLMs) undergo continuous training or additional finetuning.
Assessing the ability of LSTMs to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg · 2016
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Crowdsourcing multiple choice science questions
Johannes Welbl, Nelson F. Liu, and Matt Gardner · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, and Been Kim · 2018
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Think you have solved question answering? try arc, the ai2 reasoning challenge, 2018
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord · 2018
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Piqa: Reasoning about physical commonsense in natural language, 2019
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi · 2019
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Winogrande: An adversarial winograd schema challenge at scale, 2019
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2019
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Amnesic probing: Behavioral explanation with amnesic counterfactuals
Yanai Elazar, Shauli Ravfogel, Alon Jacovi, and Yoav Goldberg · 2020
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Scaling laws for neural language models, 2020
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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Zoom in: An introduction to circuits
Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter · 2020
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Causal mediation analysis for interpreting neural nlp: The case of gender bias, 2020
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Simas Sakenis, Jason Huang, Yaron Singer, and Stuart Shieber · 2020
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Learning which features matter: RoBERTa acquires a preference for linguistic generalizations (eventually)
Alex Warstadt, Yian Zhang, Xiaocheng Li, Haokun Liu, and Samuel R. Bowman · 2020
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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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Causal analysis of syntactic agreement mechanisms in neural language models
Matthew Finlayson, Aaron Mueller, Sebastian Gehrmann, Stuart Shieber, Tal Linzen, and Yonatan Belinkov · 2021
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Causal abstractions of neural networks
Atticus Geiger, Hanson Lu, Thomas Icard, and Christopher Potts · 2021
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Lora: Low-rank adaptation of large language models, 2021
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Refining targeted syntactic evaluation of language models
Benjamin Newman, Kai-Siang Ang, Julia Gong, and John Hewitt · 2021
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Probing classifiers: Promises, shortcomings, and advances
Yonatan Belinkov · 2022
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Causal scrubbing, a method for rigorously testing interpretability hypotheses
Lawrence Chan, Adrià Garriga-Alonso, Nicholas Goldwosky-Dill, Ryan Greenblatt, Jenny Nitishinskaya, Ansh Radhakrishnan, Buck Shlegeris, and Nate Thomas · 2022
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Word acquisition in neural language models
Tyler A. Chang and Benjamin K. Bergen · 2022
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The grammar-learning trajectories of neural language models
Leshem Choshen, Guy Hacohen, Daphna Weinshall, and Omri Abend · 2022
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Scaling instruction-finetuned language models, 2022
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei · 2022
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Probing for the usage of grammatical number
Karim Lasri, Tiago Pimentel, Alessandro Lenci, Thierry Poibeau, and Ryan Cotterell · 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, 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
Cited alongside, same era.
Neuron-level interpretation of deep nlp models: A survey, 2022
Hassan Sajjad, Nadir Durrani, and Fahim Dalvi · 2022
Cited alongside, same era.
The multiBERTs: BERT reproductions for robustness analysis
Thibault Sellam, Steve Yadlowsky, Ian Tenney, Jason Wei, Naomi Saphra, Alexander D’Amour, Tal Linzen, Jasmijn Bastings, Iulia Raluca Turc, Jacob Eisenstein, Dipanjan Das, and Ellie Pavlick · 2022
Cited alongside, same era.
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
Cited alongside, same era.
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
Progress measures for grokking via mechanistic interpretability
Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, and Jacob Steinhardt · 2023
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Attribution patching outperforms automated circuit discovery, 2023
Aaquib Syed, Can Rager, and Arthur Conmy · 2023
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Linear representations of sentiment in large language models, 2023
Curt Tigges, Oskar John Hollinsworth, Atticus Geiger, and Neel Nanda · 2023
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Explaining grokking through circuit efficiency, 2023
Vikrant Varma, Rohin Shah, Zachary Kenton, János Kramár, and Ramana Kumar · 2023
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Neurons in large language models: Dead, n-gram, positional, 2023
Elena Voita, Javier Ferrando, and Christoforos Nalmpantis · 2023
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Interpretability in the wild: a circuit for indirect object identification in GPT-2 small
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Cited alongside, same era.
Causal scrubbing: a method for rigorously testing interpretability hypotheses [redwood research]
Lawrence Chan, Adrià Garriga-Alonso, Nicholas Goldowsky-Dill, Ryan Greenblatt, Jenny Nitishinskaya, Ansh Radhakrishnan, Buck Shlegeris, and Nate Thomas · 2023
Cited alongside, same era.
Characterizing learning curves during language model pre-training: Learning, forgetting, and stability, 2023
Tyler A. Chang, Zhuowen Tu, and Benjamin K. Bergen · 2023
Cited alongside, same era.
Identifying and adapting transformer-components responsible for gender bias in an English language model
Abhijith Chintam, Rahel Beloch, Willem Zuidema, Michael Hanna, and Oskar van der Wal · 2023
Cited alongside, same era.
Evaluating the ripple effects of knowledge editing in language models, 2023
Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, and Mor Geva · 2023
Cited alongside, same era.
Towards automated circuit discovery for mechanistic interpretability
Arthur Conmy, Augustine N. Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adrià Garriga-Alonso · 2023
Cited alongside, same era.
Causal abstraction for faithful model interpretation
Atticus Geiger, Christopher Potts, and Thomas Icard · 2023
Cited alongside, same era.
Localizing model behavior with path patching, 2023
Nicholas Goldowsky-Dill, Chris MacLeod, Lucas Sato, and Aryaman Arora · 2023
Cited alongside, same era.
Kevin Ro Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt · 2023
Later among the works it cites.
Training trajectories of language models across scales
Mengzhou Xia, Mikel Artetxe, Chunting Zhou, Xi Victoria Lin, Ramakanth Pasunuru, Danqi Chen, Luke Zettlemoyer, and Veselin Stoyanov · 2023
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The claude 3 model family: Opus, sonnet, haiku, 2024
Anthropic · 2024
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Impossibility theorems for feature attribution
Blair Bilodeau, Natasha Jaques, Pang Wei Koh, and Been Kim · 2024
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Sudden drops in the loss: Syntax acquisition, phase transitions, and simplicity bias in MLMs
Angelica Chen, Ravid Shwartz-Ziv, Kyunghyun Cho, Matthew L Leavitt, and Naomi Saphra · 2024
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Information flow routes: Automatically interpreting language models at scale, 2024
Javier Ferrando and Elena Voita · 2024
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A primer on the inner workings of transformer-based language models, 2024
Javier Ferrando, Gabriele Sarti, Arianna Bisazza, and Marta R. Costa-jussà · 2024
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Olmo: Accelerating the science of language models, 2024
Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, and Hannaneh Hajishirzi · 2024
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Have faith in faithfulness: Going beyond circuit overlap when finding model mechanisms, 2024
Michael Hanna, Sandro Pezzelle, and Yonatan Belinkov · 2024
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Sparse autoencoders find highly interpretable features in language models
Robert Huben, Hoagy Cunningham, Logan Riggs Smith, Aidan Ewart, and Lee Sharkey · 2024
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Atp*: An efficient and scalable method for localizing llm behaviour to components, 2024
János Kramár, Tom Lieberum, Rohin Shah, and Neel Nanda · 2024
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Sparse feature circuits: Discovering and editing interpretable causal graphs in language models, 2024
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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The quantization model of neural scaling, 2024
Eric J. Michaud, Ziming Liu, Uzay Girit, and Max Tegmark · 2024
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Fine-tuning enhances existing mechanisms: A case study on entity tracking
Nikhil Prakash, Tamar Rott Shaham, Tal Haklay, Yonatan Belinkov, and David Bau · 2024
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Explorations of self-repair in language models, 2024
Cody Rushing and Neel Nanda · 2024
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What needs to go right for an induction head? a mechanistic study of in-context learning circuits and their formation, 2024
Aaditya K. Singh, Ted Moskovitz, Felix Hill, Stephanie C. Y. Chan, and Andrew M. Saxe · 2024
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Towards best practices of activation patching in language models: Metrics and methods
Fred Zhang and Neel Nanda · 2024
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