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Recent research has shown that Transformers with linear attention are capable of in-context learning (ICL) by implementing a linear estimator through gradient descent steps.
Language modeling with gated convolutional networks
Yann N Dauphin, Angela Fan, Michael Auli, and David Grangier · 2017
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Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
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Towards understanding the spectral bias of deep learning
Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, and Quanquan Gu · 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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Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks
Mingchen Li, Mahdi Soltanolkotabi, and Samet Oymak · 2020
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Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks
Abdulkadir Canatar, Blake Bordelon, and Cengiz Pehlevan · 2021
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Transformers can do bayesian inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al · 2021
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Scaling language models: Methods, analysis & insights from training gopher
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al · 2021
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Linear transformers are secretly fast weight programmers
Imanol Schlag, Kazuki Irie, and Jürgen Schmidhuber · 2021
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What can transformers learn in-context? a case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy S Liang, and Gregory Valiant · 2022
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Tabpfn: A transformer that solves small tabular classification problems in a second
Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter · 2022
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LoRA: Low-rank adaptation of large language models
Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
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In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al · 2022
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Shuohang Wang, Yichong Xu, Yuwei Fang, Yang Liu, Siqi Sun, Ruochen Xu, Chenguang Zhu, and Michael Zeng · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Cited alongside, same era.
An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma · 2022
Cited alongside, same era.
Transformers learn to implement preconditioned gradient descent for in-context learning
Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, and Suvrit Sra · 2023
Cited alongside, same era.
What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou · 2023
Cited alongside, same era.
A statistical perspective on retrieval-based models
Soumya Basu, Ankit Singh Rawat, and Manzil Zaheer · 2023
Cited alongside, same era.
Transformers learn in-context by gradient descent
Johannes Von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov · 2023
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Uncovering mesa-optimization algorithms in transformers
Johannes von Oswald, Eyvind Niklasson, Maximilian Schlegel, Seijin Kobayashi, Nicolas Zucchet, Nino Scherrer, Nolan Miller, Mark Sandler, Max Vladymyrov, Razvan Pascanu, et al · 2023
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How many pretraining tasks are needed for in-context learning of linear regression?
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, and Peter L Bartlett · 2023
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Trained transformers learn linear models in-context
Ruiqi Zhang, Spencer Frei, and Peter L Bartlett · 2023
Later among the works it cites.
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Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Shuming Ma, Zhifang Sui, and Furu Wei · 2023
Cited alongside, same era.
Can transformers learn optimal filtering for unknown systems?
Zhe Du, Haldun Balim, Samet Oymak, and Necmiye Ozay · 2023
Cited alongside, same era.
Hungry hungry hippos: Towards language modeling with state space models
Daniel Y Fu, Tri Dao, Khaled Kamal Saab, Armin W Thomas, Atri Rudra, and Christopher Re · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
GeminiTeam, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
Cited alongside, same era.
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
Cited alongside, same era.
In-context learning of large language models explained as kernel regression
Chi Han, Ziqi Wang, Han Zhao, and Heng Ji · 2023
Cited alongside, same era.
Atlas: Few-shot learning with retrieval augmented language models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave · 2023
Cited alongside, same era.
Nicolas Zucchet, Seijin Kobayashi, Yassir Akram, Johannes Von Oswald, Maxime Larcher, Angelika Steger, and Joao Sacramento · 2023
Later among the works it cites.
Rishabh Agarwal, Avi Singh, Lei M Zhang, Bernd Bohnet, Stephanie Chan, Ankesh Anand, Zaheer Abbas, Azade Nova, John D Co-Reyes, Eric Chu, et al · 2024
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Transformers as statisticians: Provable in-context learning with in-context algorithm selection
Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, and Song Mei · 2024
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In-context learning with transformers: Softmax attention adapts to function lipschitzness
Liam Collins, Advait Parulekar, Aryan Mokhtari, Sujay Sanghavi, and Sanjay Shakkottai · 2024
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Karthik Duraisamy · 2024
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Is mamba capable of in-context learning?
Riccardo Grazzi, Julien Siems, Simon Schrodi, Thomas Brox, and Frank Hutter · 2024
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Dissecting chain-of-thought: Compositionality through in-context filtering and learning
Yingcong Li, Kartik Sreenivasan, Angeliki Giannou, Dimitris Papailiopoulos, and Samet Oymak · 2024
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Dual operating modes of in-context learning
Ziqian Lin and Kangwook Lee · 2024
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Asymptotic theory of in-context learning by linear attention
Yue Lu, Mary I. Letey, Jacob A. Zavatone-Veth, Anindita Maiti, and Cengiz Pehlevan · 2024
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One step of gradient descent is provably the optimal in-context learner with one layer of linear self-attention
Arvind V. Mahankali, Tatsunori Hashimoto, and Tengyu Ma · 2024
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Revisiting the equivalence of in-context learning and gradient descent: The impact of data distribution
Sadegh Mahdavi, Renjie Liao, and Christos Thrampoulidis · 2024
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Can mamba learn how to learn? a comparative study on in-context learning tasks
Jongho Park, Jaeseung Park, Zheyang Xiong, Nayoung Lee, Jaewoong Cho, Samet Oymak, Kangwook Lee, and Dimitris Papailiopoulos · 2024
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Trained transformers learn linear models in-context
Ruiqi Zhang, Spencer Frei, and Peter L Bartlett · 2024
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