Fetching the paper…
Reading the bibliography…
Due in part to their discontinuous and discrete default encodings for numbers, Large Language Models (LLMs) have not yet been commonly used to process numerically-dense scientific datasets.
A New Algorithm for Data Compression
Philip Gage · 1994
Earlier work this paper cites.
All of Nonparametric Statistics
Larry Wasserman · 2006
Earlier work this paper cites.
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models
Lifu Tu, Garima Lalwani, Spandana Gella, and He He · 2007
Earlier work this paper cites.
REBOUND: an open-source multi-purpose N-body code for collisional dynamics
Rein, H. and Liu, S.-F · 2012
Earlier work this paper cites.
End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results, 2014
Jan Chorowski, Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
IAS15: a fast, adaptive, high-order integrator for gravitational dynamics, accurate to machine precision over a billion orbits
Hanno Rein and David S. Spiegel · 2015
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Generative Pretraining From Pixels
Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever · 2020
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Earlier work this paper cites.
The ERA5 global reanalysis
Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, Adrian Simmons, Cornel Soci, Saleh Abdalla, Xavier Abellan, Gianpaolo Balsamo, Peter Bechtold, Gionata Biavati, Jean Bidlot, Massimo Bonavita, Giovanna De Chiara, Per Dahlgren, Dick Dee, Michail Diamantakis, Rossana Dragani, Johannes Flemming, Richard Forbes, Manuel Fuentes, Alan Geer, Leo Haimberger, Sean Healy, Robin J. Hogan, Elías Hólm, Marta Janisková, Sarah Keeley, Patrick Laloyaux, Philippe Lopez, Cristina Lupu, Gabor Radnoti, Patricia de Rosnay, Iryna Rozum, Freja Vamborg, Sebastien Villaume, and Jean-Noël Thépaut · 2020
Earlier work this paper cites.
Learning Numeral Embeddings, 2020
Chengyue Jiang, Zhonglin Nian, Kaihao Guo, Shanbo Chu, Yinggong Zhao, Libin Shen, and Kewei Tu · 2020
Earlier work this paper cites.
Methods for Numeracy-Preserving Word Embeddings
Dhanasekar Sundararaman, Shijing Si, Vivek Subramanian, Guoyin Wang, Devamanyu Hazarika, and Lawrence Carin · 2020
Cited alongside, same era.
7 Revealing Ways AIs Fail: Neural Networks can be Disastrously Brittle, Forgetful, and Surprisingly Bad at Math
Charles Q. Choi · 2021
Cited alongside, same era.
Show Your Work: Scratchpads for Intermediate Computation with Language Models, 2021
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, and Augustus Odena · 2021
Cited alongside, same era.
Representing Numbers in NLP: a Survey and a Vision, 2021
Avijit Thawani, Jay Pujara, Pedro A. Szekely, and Filip Ilievski · 2021
Cited alongside, same era.
Exploring Length Generalization in Large Language Models, 2022
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, and Behnam Neyshabur · 2022
Cited alongside, same era.
Faith and fate: Limits of transformers on compositionality, 2023
Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jiang, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D. Hwang, Soumya Sanyal, Sean Welleck, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, and Yejin Choi · 2023
Closest in time.
Mathematical capabilities of chatgpt, 2023
Simon Frieder, Luca Pinchetti, Alexis Chevalier, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Christian Petersen, and Julius Berner · 2023
Closest in time.
Studying Large Language Model Generalization with Influence Functions, 2023
Roger Grosse, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner, Dustin Li, Esin Durmus, Ethan Perez, Evan Hubinger, Kamilė Lukošiūtė, Karina Nguyen, Nicholas Joseph, Sam McCandlish, Jared Kaplan, and Samuel R. Bowman · 2023
Closest in time.
Large language models are zero-shot time series forecasters, 2023
Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew Gordon Wilson · 2023
Closest in time.
Mathprompter: Mathematical reasoning using large language models, 2023
Shima Imani, Liang Du, and Harsh Shrivastava · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Linear Algebra with Transformers, 2022
François Charton · 2022
Cited alongside, same era.
Deep Symbolic Regression for Recurrent Sequences, 2022
Stéphane d’Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample, and François Charton · 2022
Cited alongside, same era.
What can transformers learn in-context? A Case Study of Simple Function Classes
Shivam Garg, Dimitris Tsipras, Percy S Liang, and Gregory Valiant · 2022
Cited alongside, same era.
Transformers learn shortcuts to automata
Bingbin Liu, Jordan T Ash, Surbhi Goel, Akshay Krishnamurthy, and Cyril Zhang · 2022
Cited alongside, same era.
Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets, 2022
Alethea Power, Yuri Burda, Harri Edwards, Igor Babuschkin, and Vedant Misra · 2022
Cited alongside, same era.
A Categorical Archive of ChatGPT Failures, 2023
Ali Borji · 2023
Cited alongside, same era.
Simple and Controllable Music Generation, 2023
Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, and Alexandre Défossez · 2023
Cited alongside, same era.
Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks, 2023
Tiedong Liu and Bryan Kian Hsiang Low · 2023
Closest in time.
Gpt-4 technical report, 2023
OpenAI · 2023
Closest in time.
The NLP Task Effectiveness of Long-Range Transformers, 2023
Guanghui Qin, Yukun Feng, and Benjamin Van Durme · 2023
Closest in time.
Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models, 2023
Alberto Testolin · 2023
Closest in time.
Llama 2: Open Foundation and Fine-Tuned Chat Models, 2023
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom · 2023
Closest in time.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, 2023
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou · 2023
Closest in time.
Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities, 2024
Mingyu Jin, Hua Tang, Chong Zhang, Qinkai Yu, Chengzhi Liu, Suiyuan Zhu, Yongfeng Zhang, and Mengnan Du · 2024
Closest in time.