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In-context Learning (ICL) is an emerging few-shot learning paradigm on Language Models (LMs) with inner mechanisms un-explored.
Toward semantics-based answer pinpointing
Eduard Hovy, Laurie Gerber, Ulf Hermjakob, Chin-Yew Lin, and Deepak Ravichandran · 2001
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Understanding why neural networks generalize well through gsnr of parameters
Jinlong Liu, Guoqing Jiang, Yunzhi Bai, Ting Chen, and Huayan Wang · 2001
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Learning question classifiers
Xin Li and Dan Roth · 2002
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee · 2005
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts · 2013
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Good debt or bad debt: Detecting semantic orientations in economic texts
P. Malo, A. Sinha, P. Korhonen, J. Wallenius, and P. Takala · 2014
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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Semeval-2018 task 1: Affect in tweets
Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana Kiritchenko · 2018
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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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A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, 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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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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A survey on multi-task learning
Yu Zhang and Qiang Yang · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh · 2021
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Data distributional properties drive emergent in-context learning in transformers
Stephanie Chan, Adam Santoro, Andrew Lampinen, Jane Wang, Aaditya Singh, Pierre Richemond, James McClelland, and Felix Hill · 2022
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A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui · 2022
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp · 2022
Cited alongside, same era.
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
Cited alongside, same era.
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
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.
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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Trained transformers learn linear models in-context
Ruiqi Zhang, Spencer Frei, and Peter L Bartlett · 2023
Later among the works it cites.
Rishabh Agarwal, Avi Singh, Lei M Zhang, Bernd Bohnet, Luis Rosias, Stephanie CY Chan, Biao Zhang, Aleksandra Faust, and Hugo Larochelle · 2024
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Llama 3 model card
AI@Meta · 2024
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In-context learning with long-context models: An in-depth exploration
Amanda Bertsch, Maor Ivgi, Uri Alon, Jonathan Berant, Matthew R Gormley, and Graham Neubig · 2024
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Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, Hyunsoo Cho, Hwiyeol Jo, Sang-Woo Lee, Sang-goo Lee, and Taeuk Kim · 2022
Cited alongside, same era.
The falcon series of open language models
Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Mérouane Debbah, Étienne Goffinet, Daniel Hesslow, Julien Launay, Quentin Malartic, et al · 2023
Cited alongside, same era.
Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al · 2023
Cited alongside, same era.
Data curation alone can stabilize in-context learning
Ting-Yun Chang and Robin Jia · 2023
Cited alongside, same era.
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 · 2023
Cited alongside, same era.
Demystifying prompts in language models via perplexity estimation
Hila Gonen, Srini Iyer, Terra Blevins, Noah A Smith, and Luke Zettlemoyer · 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.
Similarity of neural network models: A survey of functional and representational measures
Max Klabunde, Tobias Schumacher, Markus Strohmaier, and Florian Lemmerich · 2023
Cited alongside, same era.
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Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu · 2024
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Token-based decision criteria are suboptimal in in-context learning
Hakaze Cho, Yoshihiro Sakai, Mariko Kato, Kenshiro Tanaka, Akira Ishii, and Naoya Inoue · 2024
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What makes a good order of examples in in-context learning
Qi Guo, Leiyu Wang, Yidong Wang, Wei Ye, and Shikun Zhang · 2024
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The platonic representation hypothesis
Minyoung Huh, Brian Cheung, Tongzhou Wang, and Phillip Isola · 2024
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An information-theoretic analysis of in-context learning
Hong Jun Jeon, Jason D Lee, Qi Lei, and Benjamin Van Roy · 2024
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In-context learning learns label relationships but is not conventional learning
Jannik Kossen, Yarin Gal, and Tom Rainforth · 2024
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Let’s learn step by step: Enhancing in-context learning ability with curriculum learning
Yinpeng Liu, Jiawei Liu, Xiang Shi, Qikai Cheng, and Wei Lu · 2024
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The mechanistic basis of data dependence and abrupt learning in an in-context classification task
Gautam Reddy · 2024
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Efficient streaming language models with attention sinks
Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, and Mike Lewis · 2024
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Distributed rule vectors is a key mechanism in large language models’ in-context learning
Bowen Zheng, Ming Ma, Zhongqiao Lin, and Tianming Yang · 2024
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