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In-context learning (ICL) ability has emerged with the increasing scale of large language models (LLMs), enabling them to learn input-label mappings from demonstrations and perform well on downstream tasks.
“Semantic parsing on freebase from question-answer pairs,”
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang, · 2013
Earlier work this paper cites.
“Adam: A Method for Stochastic Optimization,”
Diederik P. Kingma and Jimmy Ba, · 2014
Earlier work this paper cites.
“A large annotated corpus for learning natural language inference,”
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning, · 2015
Earlier work this paper cites.
“A broad-coverage challenge corpus for sentence understanding through inference,”
Adina Williams, Nikita Nangia, and Samuel R Bowman, · 2017
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Cer, Daniel, et al., · 2017
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“Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension,”
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer, · 2017
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“Representation learning with contrastive predictive coding,”
Aaron van den Oord, Yazhe Li, and Oriol Vinyals, · 2018
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“Natural questions: a benchmark for question answering research,”
Kwiatkowski, Tom, et al., · 2019
Earlier work this paper cites.
“Language models are few-shot learners,”
Tom Brown, Benjamin Mann, Nick Ryder, et al., · 2020
Earlier work this paper cites.
“Dense passage retrieval for open-domain question answering,”
Karpukhin, Vladimir, et al., · 2020
Cited alongside, same era.
“Retrieval-augmented generation for knowledge-intensive nlp tasks,”
Lewis, Patrick, et al., · 2020
Cited alongside, same era.
“Question and answer test-train overlap in open-domain question answering datasets,”
Patrick Lewis, Pontus Stenetorp, and Sebastian Riedel, · 2020
Cited alongside, same era.
“What makes good in-context examples for gpt- 3 3 ?,”
Jiachang Liu, Dinghan Shen, Yizhe Zhang, et al., · 2021
Cited alongside, same era.
“Calibrate before use: Improving few-shot performance of language models,”
Zihao Zhao, Eric Wallace, Shi Feng, et al., · 2021
Cited alongside, same era.
“Demonstrate-search-predict: Composing retrieval and language models for knowledge-intensive nlp,”
Khattab, Omar, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, and Matei Zaharia, · 2022
Later among the works it cites.
“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
Later among the works it cites.
“Recitation-augmented language models,”
Zhiqing Sun, Xuezhi Wang, Yi Tay, Yiming Yang, and Denny Zhou, · 2022
Later among the works it cites.
“Connecting large language models with evolutionary algorithms yields powerful prompt optimizers,”
Qingyan Guo, Rui Wang, et al., · 2023
Closest in time.
“Specialist or generalist? instruction tuning for specific NLP tasks,”
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“A survey for in-context learning,”
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui, · 2022
Cited alongside, same era.
“Demystifying prompts in language models via perplexity estimation,”
Hila Gonen, Srini Iyer, Terra Blevins, Noah A Smith, and Luke Zettlemoyer, · 2022
Cited alongside, same era.
“Rethinking the role of demonstrations: What makes in-context learning work?,”
Sewon Min, Xinxi Lyu, et al., · 2022
Cited alongside, same era.
“Measuring and Narrowing the Compositionality Gap in Language Models,”
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, and Mike Lewis, · 2022
Cited alongside, same era.
Chufan Shi, Yixuan Su, Cheng Yang, Yujiu Yang, and Deng Cai, · 2023
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“Larger language models do in-context learning differently,”
Jerry Wei, Jason Wei, Yi Tay, et al., · 2023
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“Benchmarking large language models for news summarization,”
Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, and Tatsunori B Hashimoto, · 2023
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“Llama 2: Open foundation and fine-tuned chat models,”
Hugo Touvron, Louis Martin, Kevin Stone, et al., · 2023
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“Replug: Retrieval-augmented black-box language models,”
Weijia Shi, Sewon Min, Michihiro Yasunaga, et al., · 2023
Closest in time.