Fetching the paper…
Reading the bibliography…
The interest in linear complexity models for large language models is on the rise, although their scaling capacity remains uncertain.
Socialiqa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, and Yejin Choi. 2019 · 1904
Earlier work this paper cites.
Boolq: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019 · 1905
Earlier work this paper cites.
Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 1905
Earlier work this paper cites.
Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2019 · 1907
Earlier work this paper cites.
Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2019 · 1911
Earlier work this paper cites.
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 · 2001
Earlier work this paper cites.
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 · 2010
Earlier work this paper cites.
The wikitext long term dependency language modeling dataset
Stephen Merity. 2016 · 2016
Earlier work this paper cites.
The lambada dataset: Word prediction requiring a broad discourse context
Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Quan Ngoc Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. 2016 · 2016
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Earlier work this paper cites.
Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018 · 2018
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
Earlier work this paper cites.
Hippo: Recurrent memory with optimal polynomial projections
Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, and Christopher Ré. 2020 · 2020
Earlier work this paper cites.
Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret. 2020 · 2020
Earlier work this paper cites.
Rethinking attention with performers
Krzysztof Marcin Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Quincy Davis, Afroz Mohiuddin, Lukasz Kaiser, David Benjamin Belanger, Lucy J Colwell, and Adrian Weller. 2021 · 2021
Cited alongside, same era.
A framework for few-shot language model evaluation
Leo Gao, Jonathan Tow, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Kyle McDonell, Niklas Muennighoff, et al. 2021 · 2021
Cited alongside, same era.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Cited alongside, same era.
Danny Hernandez, Jared Kaplan, Tom Henighan, and Sam McCandlish. 2021 · 2021
Cited alongside, same era.
cosformer: Rethinking softmax in attention
Zhen Qin, Weixuan Sun, Hui Deng, Dongxu Li, Yunshen Wei, Baohong Lv, Junjie Yan, Lingpeng Kong, and Yiran Zhong. 2021 · 2021
Cited alongside, same era.
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao. 2023 · 2023
Later among the works it cites.
Scaling laws for single-agent reinforcement learning
Jacob Hilton, Jie Tang, and John Schulman. 2023 · 2023
Later among the works it cites.
OpanAI. 2023 · 2023
Later among the works it cites.
Resurrecting recurrent neural networks for long sequences
Antonio Orvieto, Samuel L Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, and Soham De. 2023 · 2023
Later among the works it cites.
Accelerating toeplitz neural network with constant-time inference complexity
Zhen Qin and Yiran Zhong. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Unified scaling laws for routed language models
Aidan Clark, Diego de Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake Hechtman, Trevor Cai, Sebastian Borgeaud, et al. 2022 · 2022
Cited alongside, same era.
Hungry hungry hippos: Towards language modeling with state space models
Daniel Y Fu, Tri Dao, Khaled K Saab, Armin W Thomas, Atri Rudra, and Christopher Ré. 2022 · 2022
Cited alongside, same era.
On the parameterization and initialization of diagonal state space models
Albert Gu, Karan Goel, Ankit Gupta, and Christopher Ré. 2022 · 2022
Cited alongside, same era.
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 · 2022
Cited alongside, same era.
Transformer quality in linear time
Weizhe Hua, Zihang Dai, Hanxiao Liu, and Quoc V Le. 2022 · 2022
Cited alongside, same era.
Neural architecture search on efficient transformers and beyond
Zexiang Liu, Dong Li, Kaiyue Lu, Zhen Qin, Weixuan Sun, Jiacheng Xu, and Yiran Zhong. 2022 · 2022
Cited alongside, same era.
Scrolls: Standardized comparison over long language sequences
Uri Shaham, Elad Segal, Maor Ivgi, Avia Efrat, Ori Yoran, Adi Haviv, Ankit Gupta, Wenhan Xiong, Mor Geva, Jonathan Berant, et al. 2022 · 2022
Cited alongside, same era.
Retentive network: A successor to transformer for large language models
Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, and Furu Wei. 2023 · 2023
Later among the works it cites.
Gated linear attention transformers with hardware-efficient training
Songlin Yang, Bailin Wang, Yikang Shen, Rameswar Panda, and Yoon Kim. 2023 · 2023
Later among the works it cites.
Efficient attention via control variates
Lin Zheng, Jianbo Yuan, Chong Wang, and Lingpeng Kong. 2023 · 2023
Later among the works it cites.
Deepseek llm: Scaling open-source language models with longtermism
Xiao Bi, Deli Chen, Guanting Chen, Shanhuang Chen, Damai Dai, Chengqi Deng, Honghui Ding, Kai Dong, and Qiushi Du. 2024 · 2024
Closest in time.
Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality
Tri Dao and Albert Gu. 2024 · 2024
Closest in time.
Scaling and evaluating sparse autoencoders
Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu. 2024 · 2024
Closest in time.
Ruler: What’s the real context size of your long-context language models?
Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, and Boris Ginsburg. 2024 · 2024
Closest in time.
Scaling laws for downstream task performance of large language models
Berivan Isik, Natalia Ponomareva, Hussein Hazimeh, Dimitris Paparas, Sergei Vassilvitskii, and Sanmi Koyejo. 2024 · 2024
Closest in time.
Unraveling the mystery of scaling laws: Part i
Hui Su, Zhi Tian, Xiaoyu Shen, and Xunliang Cai. 2024 · 2024
Closest in time.
Fla: A triton-based library for hardware-efficient implementations of linear attention mechanism
Songlin Yang and Yu Zhang. 2024 · 2024
Closest in time.