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Language Models (LMs) often must integrate facts they memorized in pretraining with new information that appears in a given context.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Counterfactual story reasoning and generation
Lianhui Qin, Antoine Bosselut, Ari Holtzman, Chandra Bhagavatula, Elizabeth Clark, and Yejin Choi. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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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 · 2020
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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, Shawn Presser, and Connor Leahy. 2020 · 2020
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Interpreting gpt: The logit lens
Nostalgebraist. 2020 · 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 · 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 · 2021
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Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2021 · 2021
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Knowledge neurons in pretrained transformers
Damai Dai, Li Dong, Yaru Hao, Zhifang Sui, Baobao Chang, and Furu Wei. 2022 · 2022
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Analyzing transformers in embedding space
Guy Dar, Mor Geva, Ankit Gupta, and Jonathan Berant. 2022 · 2022
Cited alongside, same era.
State-of-the-art generalisation research in nlp: a taxonomy and review
Dieuwke Hupkes, Mario Giulianelli, Verna Dankers, Mikel Artetxe, Yanai Elazar, Tiago Pimentel, Christos Christodoulopoulos, Karim Lasri, Naomi Saphra, Arabella Sinclair, et al. 2022 · 2022
Cited alongside, same era.
Large language models struggle to learn long-tail knowledge
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2022 · 2022
Cited alongside, same era.
The singular value decompositions of transformer weight matrices are highly interpretable
Beren Millidge and Sid Black. 2022 · 2022
Cited alongside, same era.
Rethinking the role of demonstrations: What makes in-context learning work?
Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. 2023 · 2023
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Dissecting recall of factual associations in auto-regressive language models
Mor Geva, Jasmijn Bastings, Katja Filippova, and Amir Globerson. 2023 · 2023
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Understanding transformer memorization recall through idioms
Adi Haviv, Ido Cohen, Jacob Gidron, Roei Schuster, Yoav Goldberg, and Mor Geva. 2023 · 2023
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Feed-forward blocks control contextualization in masked language models
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui. 2023 · 2023
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Counterfactual reasoning: Testing language models’ understanding of hypothetical scenarios
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Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Transformerlens
Neel Nanda. 2022 · 2022
Cited alongside, same era.
Impact of pretraining term frequencies on few-shot reasoning
Yasaman Razeghi, Robert L. Logan IV au2, Matt Gardner, and Sameer Singh. 2022 · 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 · 2022
Cited alongside, same era.
An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2022 · 2022
Cited alongside, same era.
Eliciting latent predictions from transformers with the tuned lens
Nora Belrose, Zach Furman, Logan Smith, Danny Halawi, Igor Ostrovsky, Lev McKinney, Stella Biderman, and Jacob Steinhardt. 2023 · 2023
Cited alongside, same era.
Jiaxuan Li, Lang Yu, and Allyson Ettinger. 2023 · 2023
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Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2023 · 2023
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A mechanism for solving relational tasks in transformer language models
Jack Merullo, Carsten Eickhoff, and Ellie Pavlick. 2023 · 2023
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Progress measures for grokking via mechanistic interpretability
Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, and Jacob Steinhardt. 2023 · 2023
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Larger language models do in-context learning differently
Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, and Tengyu Ma. 2023 · 2023
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