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The capabilities and limitations of Large Language Models have been sketched out in great detail in recent years, providing an intriguing yet conflicting picture.
The influence curve and its role in robust estimation
Frank R. Hampel · 1974
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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 · 2001
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Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
N. Halko, P. G. Martinsson, and J. A. Tropp · 2011
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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Dask: Library for dynamic task scheduling , 2016
Dask Development Team · 2016
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Pointer sentinel mixture models, 2016
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Understanding black-box predictions via influence functions
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RACE: Large-scale ReAding comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy · 2017
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Fast approximate natural gradient descent in a kronecker factored eigenbasis
Thomas George, César Laurent, Xavier Bouthillier, Nicolas Ballas, and Pascal Vincent · 2018
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DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
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Relatif: Identifying explanatory training samples via relative influence
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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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris 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
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Estimating training data influence by tracing gradient descent
Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2021
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Palm: Scaling language modeling with pathways, 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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An empirical analysis of compute-optimal large language model training
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Thomas Hennigan, Eric Noland, Katherine Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karén Simonyan, Erich Elsen, Oriol Vinyals, Jack Rae, and Laurent Sifre · 2022
To code, or not to code? exploring impact of code in pre-training, 2024
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Training data attribution via approximate unrolled differentiation, 2024
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Benchmark probing: Investigating data leakage in large language models
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In-context learning and induction heads
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Impact of pretraining term frequencies on few-shot numerical reasoning
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou · 2022
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Towards monosemanticity: Decomposing language models with dictionary learning
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The debate over understanding in ai’s large language models
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Transformers can do arithmetic with the right embeddings, 2024
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Fine-tuning enhances existing mechanisms: A case study on entity tracking
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What needs to go right for an induction head? a mechanistic study of in-context learning circuits and their formation
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Reasoning or reciting? exploring the capabilities and limitations of language models through counterfactual tasks
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