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In-Context Learning (ICL) typically utilizes classification criteria from output probabilities of manually selected label tokens.
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White. 1989 · 1989
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Toward semantics-based answer pinpointing
Eduard Hovy, Laurie Gerber, Ulf Hermjakob, Chin-Yew Lin, and Deepak Ravichandran. 2001 · 2001
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Learning question classifiers
Xin Li and Dan Roth. 2002 · 2002
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
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Good debt or bad debt: Detecting semantic orientations in economic texts
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SemEval-2014 task 4: Aspect based sentiment analysis
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
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Semeval-2017 task 4: Sentiment analysis in twitter
Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017 · 2017
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Hate Speech Dataset from a White Supremacy Forum
Ona de Gibert, Naiara Perez, Aitor García-Pablos, and Montse Cuadros. 2018 · 2018
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Semeval-2018 task 1: Affect in tweets
Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana Kiritchenko. 2018 · 2018
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SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in Twitter
Valerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, and Manuela Sanguinetti. 2019 · 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 · 2019
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019 · 2019
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Investigating societal biases in a poetry composition system
Emily Sheng and David Uthus. 2020 · 2020
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Surface form competition: Why the highest probability answer isn’t always right
Ari Holtzman, Peter West, Vered Shwartz, Yejin Choi, and Luke Zettlemoyer. 2021 · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 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 · 2021
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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 · 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 · 2022
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Mitigating label biases for in-context learning
Yu Fei, Yifan Hou, Zeming Chen, and Antoine Bosselut. 2023 · 2023
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Demystifying prompts in language models via perplexity estimation
Hila Gonen, Srini Iyer, Terra Blevins, Noah A Smith, and Luke Zettlemoyer. 2023 · 2023
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Pre-training to learn in context
Yuxian Gu, Li Dong, Furu Wei, and Minlie Huang. 2023 · 2023
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Understanding in-context learning via supportive pretraining data
Xiaochuang Han, Daniel Simig, Todor Mihaylov, Yulia Tsvetkov, Asli Celikyilmaz, and Tianlu Wang. 2023 · 2023
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Zhixiong Han, Yaru Hao, Li Dong, Yutao Sun, and Furu Wei. 2022 · 2022
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Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. 2022 · 2022
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Opt-iml: Scaling language model instruction meta learning through the lens of generalization
Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Kurt Shuster, Tianlu Wang, Qing Liu, Punit Singh Koura, et al. 2022 · 2022
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Hyuhng Joon Kim, Hyunsoo Cho, Junyeob Kim, Taeuk Kim, Kang Min Yoo, and Sang-goo Lee. 2022 · 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 · 2022
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Metaicl: Learning to learn in context
Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022b · 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. 2022c · 2022
Cited alongside, same era.
Nearest neighbor zero-shot inference
Weijia Shi, Julian Michael, Suchin Gururangan, and Luke Zettlemoyer. 2022 · 2022
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Roee Hendel, Mor Geva, and Amir Globerson. 2023 · 2023
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Generative calibration for in-context learning
Zhongtao Jiang, Yuanzhe Zhang, Cao Liu, Jun Zhao, and Kang Liu. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Symbol tuning improves in-context learning in language models
Jerry Wei, Le Hou, Andrew Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, et al. 2023 · 2023
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Batch calibration: Rethinking calibration for in-context learning and prompt engineering
Han Zhou, Xingchen Wan, Lev Proleev, Diana Mincu, Jilin Chen, Katherine Heller, and Subhrajit Roy. 2023 · 2023
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Enhancing in-context learning via linear probe calibration
Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, and Tianyi Chen. 2024 · 2024
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Llama 3 model card
AI@Meta. 2024 · 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 · 2024
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Mind your format: Towards consistent evaluation of in-context learning improvements
Anton Voronov, Lena Wolf, and Max Ryabinin. 2024 · 2024
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In-context example ordering guided by label distributions
Zhichao Xu, Daniel Cohen, Bei Wang, and Vivek Srikumar. 2024 · 2024
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Noisyicl: A little noise in model parameters calibrates in-context learning
Yufeng Zhao, Yoshihiro Sakai, and Naoya Inoue. 2024 · 2024
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