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In-context learning (ICL) is an astonishing emergent ability of large language models (LLMs).
Borderline-smote: a new over-sampling method in imbalanced data sets learning
Hui Han, Wen-Yuan Wang, and Bing-Huan Mao · 2005
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Learning from imbalanced data
Haibo He and Edwardo A Garcia · 2009
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Ensemble of exemplar-svms for object detection and beyond
Tomasz Malisiewicz, Abhinav Gupta, and Alexei A Efros · 2011
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Learning from imbalanced data: open challenges and future directions
Bartosz Krawczyk · 2016
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Learning from class-imbalanced data: Review of methods and applications
Guo Haixiang, Li Yijing, Jennifer Shang, Gu Mingyun, Huang Yuanyue, and Gong Bing · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Learning to model the tail
Yu-Xiong Wang, Deva Ramanan, and Martial Hebert · 2017
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Survey of the state of the art in natural language generation: Core tasks, applications and evaluation
Albert Gatt and Emiel Krahmer · 2018
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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Learning data manipulation for augmentation and weighting
Zhiting Hu, Bowen Tan, Russ R Salakhutdinov, Tom M Mitchell, and Eric P Xing · 2019
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Learning to balance: Bayesian meta-learning for imbalanced and out-of-distribution tasks
Hae Beom Lee, Hayeon Lee, Donghyun Na, Saehoon Kim, Minseop Park, Eunho Yang, and Sung Ju Hwang · 2019
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Meta-weight-net: Learning an explicit mapping for sample weighting
Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng · 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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Evaluation of text generation: A survey
Asli Celikyilmaz, Elizabeth Clark, and Jianfeng Gao · 2020
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Mesa: boost ensemble imbalanced learning with meta-sampler
Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, and Yi Chang · 2020
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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 · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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What makes good in-context examples for gpt- 3 3 ?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen · 2021
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Noisy channel language model prompting for few-shot text classification
Sewon Min, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2021
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Automatic code generation using pre-trained language models
Luis Perez, Lizi Ottens, and Sudharshan Viswanathan · 2021
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Spot: Better frozen model adaptation through soft prompt transfer
Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou, and Daniel Cer · 2021
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Differentiable prompt makes pre-trained language models better few-shot learners
Ningyu Zhang, Luoqiu Li, Xiang Chen, Shumin Deng, Zhen Bi, Chuanqi Tan, Fei Huang, and Huajun Chen · 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
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 · 2022
Cited alongside, same era.
Attentional mixtures of soft prompt tuning for parameter-efficient multi-task knowledge sharing
Akari Asai, Mohammadreza Salehi, Matthew E Peters, and Hannaneh Hajishirzi · 2022
Cited alongside, same era.
Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al · 2022
Cited alongside, same era.
What can transformers learn in-context? a case study of simple function classes
Gpt understands, too
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang · 2023
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Towards enhancing in-context learning for code generation
Jia Li, Yunfei Zhao, Yongmin Li, Ge Li, and Zhi Jin · 2023
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Using in-context learning to improve dialogue safety
Nicholas Meade, Spandana Gella, Devamanyu Hazarika, Prakhar Gupta, Di Jin, Siva Reddy, Yang Liu, and Dilek Hakkani-Tür · 2023
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Sources of hallucination by large language models on inference tasks
Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Javad Hosseini, Mark Johnson, and Mark Steedman · 2023
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Tuning language models as training data generators for augmentation-enhanced few-shot learning
Yu Meng, Martin Michalski, Jiaxin Huang, Yu Zhang, Tarek Abdelzaher, and Jiawei Han · 2023
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Shivam Garg, Dimitris Tsipras, Percy S Liang, and Gregory Valiant · 2022
Cited alongside, same era.
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel · 2022
Cited alongside, same era.
Peft: State-of-the-art parameter-efficient fine-tuning methods
Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, and Sayak Paul · 2022
Cited alongside, same era.
On the joint-effect of class imbalance and overlap: a critical review
Miriam Seoane Santos, Pedro Henriques Abreu, Nathalie Japkowicz, Alberto Fernández, Carlos Soares, Szymon Wilk, and Joao Santos · 2022
Cited alongside, same era.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
Cited alongside, same era.
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.
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
Cited alongside, same era.
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Towards making the most of chatgpt for machine translation
Keqin Peng, Liang Ding, Qihuang Zhong, Li Shen, Xuebo Liu, Min Zhang, Yuanxin Ouyang, and Dacheng Tao · 2023
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Bayesian optimization of catalysts with in-context learning
Mayk Caldas Ramos, Shane S Michtavy, Marc D Porosoff, and Andrew D White · 2023
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Vipula Rawte, Prachi Priya, SM Tonmoy, SM Zaman, Amit Sheth, and Amitava Das · 2023
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Execution-based code generation using deep reinforcement learning
Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, and Chandan K Reddy · 2023
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How does in-context learning help prompt tuning?
Simeng Sun, Yang Liu, Dan Iter, Chenguang Zhu, and Mohit Iyyer · 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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Emergent analogical reasoning in large language models
Taylor Webb, Keith J Holyoak, and Hongjing Lu · 2023
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Codet5+: Open code large language models for code understanding and generation
Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi DQ Bui, Junnan Li, and Steven CH Hoi · 2023
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The learnability of in-context learning
Noam Wies, Yoav Levine, and Amnon Shashua · 2023
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Multitask prompt tuning enables parameter-efficient transfer learning
Zhen Wang, Rameswar Panda, Leonid Karlinsky, Rogerio Feris, Huan Sun, and Yoon Kim · 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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Neural collapse inspired attraction–repulsion-balanced loss for imbalanced learning
Liang Xie, Yibo Yang, Deng Cai, and Xiaofei He · 2023
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In-context instruction learning
Seonghyeon Ye, Hyeonbin Hwang, Sohee Yang, Hyeongu Yun, Yireun Kim, and Minjoon Seo · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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Trained transformers learn linear models in-context
Ruiqi Zhang, Spencer Frei, and Peter L Bartlett · 2023
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Progressive-hint prompting improves reasoning in large language models
Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, and Yu Li · 2023
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How language model hallucinations can snowball
Muru Zhang, Ofir Press, William Merrill, Alisa Liu, and Noah A Smith · 2023
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Tsk fuzzy system fusion at sensitivity-ensemble-level for imbalanced data classification
Yuanpeng Zhang, Guanjin Wang, Xiuyu Huang, and Weiping Ding · 2023
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Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x
Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, et al · 2023
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