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We examine how large language models (LLMs) generalize from abstract declarative statements in their training data.
The influence curve and its role in robust estimation
Frank R Hampel · 1974
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Superintelligence: Paths, Dangers, Strategies
Nick Bostrom · 2014
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Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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What failure looks like, Mar 2019
Paul F. Christiano · 2019
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A constructive prediction of the generalization error across scales
Jonathan S Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, and Nir Shavit · 2019
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Universal adversarial triggers for attacking and analyzing nlp
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Scaling laws for autoregressive generative modeling
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al · 2020
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Scaling laws for neural language models
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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On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Data and parameter scaling laws for neural machine translation
Mitchell A Gordon, Kevin Duh, and Jared Kaplan · 2021
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Unsolved problems in ml safety
Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt · 2021
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Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
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Predictability and surprise in large generative models
Deep Ganguli, Danny Hernandez, Liane Lovitt, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova Dassarma, Dawn Drain, Nelson Elhage, et al · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
Eight things to know about large language models
Samuel R Bowman · 2023
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Studying large language model generalization with influence functions
Roger Grosse, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner, Dustin Li, Esin Durmus, Ethan Perez, et al · 2023
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Evaluating language-model agents on realistic autonomous tasks
Megan Kinniment, LJK Sato, H Du, B Goodrich, M Hasin, L Chan, LH Miles, TR Lin, H Wijk, J Burget, et al · 2023
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Bad: Bias detection for large language models in the context of candidate screening
Nam Ho Koh, Joseph Plata, and Joyce Chai · 2023
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Out-of-context meta-learning in large language models
Dmitrii Krasheninnikov, Egor Krasheninnikov, and David Krueger · 2023
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Datamodels: Understanding predictions with data and data with predictions
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, and Aleksander Madry · 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al · 2022
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Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
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Claude 2
Anthropic · 2023
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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
Cited alongside, same era.
Taken out of context: On measuring situational awareness in llms
Lukas Berglund, Asa Cooper Stickland, Mikita Balesni, Max Kaufmann, Meg Tong, Tomasz Korbak, Daniel Kokotajlo, and Owain Evans
Cited in the paper.
The reversal curse: Llms trained on ”a is b” fail to learn ”b is a”, 2023b
Lukas Berglund, Meg Tong, Max Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, and Owain Evans
Cited in the paper.
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Inverse scaling: When bigger isn’t better
Ian R McKenzie, Alexander Lyzhov, Michael Pieler, Alicia Parrish, Aaron Mueller, Ameya Prabhu, Euan McLean, Aaron Kirtland, Alexis Ross, Alisa Liu, et al · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Are emergent abilities of large language models a mirage?
Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Poisoning language models during instruction tuning
Alexander Wan, Eric Wallace, Sheng Shen, and Dan Klein · 2023
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