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reasoning-8

Analogical Similarity

3260 papers • 126 benchmarks • 313 datasets

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Scaling Language Models: Methods, Analysis & Insights from Training Gopher

A. Mensch, Katie Millican, Roman Ring, Eliza Rutherford, Jacob Menick, Sebastian Borgeaud, Aida Nematzadeh, O. Vinyals, K. Simonyan, Yujia Li, Igor Babuschkin, Po-Sen Huang, Johannes Welbl, K. Kavukcuoglu, D. Hassabis, James Bradbury, Chris Dyer, L. Sifre, George van den Driessche, Erich Elsen, D. Budden, G. Irving, Laura Weidinger, Maribeth Rauh, Iason Gabriel, William S. Isaac, Saffron Huang, Lisa Anne Hendricks, I. Higgins, Diego de Las Casas, Jack W. Rae, Siddhant M. Jayakumar, Xiang Lorraine Li, Francis Song, Cyprien de Masson d'Autume, Amy Wu, Angeliki Lazaridou, A. Kuncoro, Michela Paganini, Trevor Cai, Jordan Hoffmann, John Aslanides, Sarah Henderson, Susannah Young, Tom Hennigan, Albin Cassirer, Richard Powell, Amelia Glaese, Sumanth Dathathri, Jonathan Uesato, John F. J. Mellor, Antonia Creswell, Nat McAleese, Elena Buchatskaya, Esme Sutherland, Lena Martens, E. Gribovskaya, Domenic Donato, Jean-Baptiste Lespiau, M. Tsimpoukelli, N. Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Tobias Pohlen, Z. Gong, Daniel Toyama, Tayfun Terzi, Vladimir Mikulik, Aidan Clark, Aurelia Guy, Chris Jones, Matthew G. Johnson, Blake A. Hechtman, Edward Lockhart, Simon Osindero, Laura Rimell, Kareem W. Ayoub, J. Stanway, L. Bennett

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

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Tue Dec 07 2021

This paper presents an analysis of Transformer-based language model performance across a wide range of model scales -- from models with tens of millions of parameters up to a 280 billion parameter model called Gopher.

1527 0

Paper Graph

Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes

Dafna Shahaf, Oren Sultan•Thu Oct 20 2022

An interpretable, scalable algorithm is developed that can extract analogies from a large dataset of procedural texts, achieving 79% precision, and it is demonstrated that the algorithm is robust to paraphrasing the input texts.

31 0

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Data Level Lottery Ticket Hypothesis for Vision Transformers

Geng Yuan, Yanzhi Wang, Xuan Shen, Zhenglun Kong, Minghai Qin, Peiyan Dong, Xin Meng, Hao Tang, Xiaolong Ma•Tue Nov 01 2022

This paper shows that the conventional winning ticket is hard to find at weight level of ViTs by existing methods, and generalizes the LTH for ViTs to input data consisting of image patches inspired by the input dependence ofViTs, which verifies the integrity of the theory.

7 0

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Training Compute-Optimal Large Language Models

A. Mensch, Katie Millican, Eliza Rutherford, Sebastian Borgeaud, O. Vinyals, K. Simonyan, Johannes Welbl, L. Sifre, George van den Driessche, Erich Elsen, Lisa Anne Hendricks, Diego de Las Casas, Jack W. Rae, Eric Noland, Trevor Cai, Jordan Hoffmann, Tom Hennigan, Elena Buchatskaya, Aidan Clark, Aurelia Guy, Simon Osindero, Bogdan Damoc•Mon Mar 28 2022

This work trains a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4$\times$ more more data, and reaches a state-of-the-art average accuracy, greater than a 7% improvement over Gopher.

2766 0

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Analogical Similarity | State-of-the-Art