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We investigate the transformer's capability to simulate the training process of deep models via in-context learning (ICL), i.e., in-context deep learning.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Depth-width tradeoffs in approximating natural functions with neural networks
Itay Safran and Ohad Shamir · 2017
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Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 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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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma · 2021
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Exploring length generalization in large language models
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, and Behnam Neyshabur · 2022
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Improving in-context few-shot learning via self-supervised training
Mingda Chen, Jingfei Du, Ramakanth Pasunuru, Todor Mihaylov, Srini Iyer, Veselin Stoyanov, and Zornitsa Kozareva · 2022
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Why can gpt learn in-context? language models implicitly perform gradient descent as meta-optimizers
Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Shuming Ma, Zhifang Sui, and Furu Wei · 2022
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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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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
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2022
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On the effect of pretraining corpora on in-context learning by a large-scale language model
Seongjin Shin, Sang-Woo Lee, Hwijeen Ahn, Sungdong Kim, HyoungSeok Kim, Boseop Kim, Kyunghyun Cho, Gichang Lee, Woomyoung Park, Jung-Woo Ha, et al · 2022
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Ground-truth labels matter: A deeper look into input-label demonstrations
Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, Hyunsoo Cho, Hwiyeol Jo, Sang-Woo Lee, Sang-goo Lee, and Taeuk Kim · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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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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Larger language models do in-context learning differently
Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, et al · 2023
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Diffusion models: A comprehensive survey of methods and applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang · 2023
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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 · 2023
Cited alongside, same era.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
Cited alongside, same era.
Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data
Minshuo Chen, Kaixuan Huang, Tuo Zhao, and Mengdi Wang · 2023
Cited alongside, same era.
Pre-training to learn in context
Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang · 2023
Cited alongside, same era.
The closeness of in-context learning and weight shifting for softmax regression
Shuai Li, Zhao Song, Yu Xia, Tong Yu, and Tianyi Zhou · 2023
Cited alongside, same era.
Trainable transformer in transformer
Abhishek Panigrahi, Sadhika Malladi, Mengzhou Xia, and Sanjeev Arora · 2023
Cited alongside, same era.
In-context learning through the bayesian prism
Madhur Panwar, Kabir Ahuja, and Navin Goyal · 2023
Cited alongside, same era.
In-context pretraining: Language modeling beyond document boundaries
Weijia Shi, Sewon Min, Maria Lomeli, Chunting Zhou, Margaret Li, Victoria Lin, Noah A Smith, Luke Zettlemoyer, Scott Yih, and Mike Lewis · 2023
Cited alongside, same era.
Ruiqi Zhang, Spencer Frei, and Peter L Bartlett · 2023
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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, Qiushi Du, Zhe Fu, et al · 2024
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Tutorial on diffusion models for imaging and vision
Stanley H Chan · 2024
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An overview of diffusion models: Applications, guided generation, statistical rates and optimization
Minshuo Chen, Song Mei, Jianqing Fan, and Mengdi Wang · 2024
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Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
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Are transformers with one layer self-attention using low-rank weight matrices universal approximators?
Tokio Kajitsuka and Issei Sato · 2024
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In-context learning on function classes unveiled for transformers
Zhijie Wang, Bo Jiang, and Shuai Li · 2024
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The learnability of in-context learning
Noam Wies, Yoav Levine, and Amnon Shashua · 2024
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