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In-context learning (ICL) leverages in-context examples as prompts for the predictions of Large Language Models (LLMs).
An analysis of approximations for maximizing submodular set functions—i
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Learning to parse database queries using inductive logic programming
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Submodular functions, matroids, and certain polyhedra
J. Edmonds · 2003
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Unsupervised construction of large paraphrase corpora: Exploiting massively parallel news sources
W. Dolan, C. Quirk, C. Brockett, and B. Dolan · 2004
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The fifth pascal recognizing textual entailment challenge
L. Bentivogli, P. Clark, I. Dagan, and D. Giampiccolo · 2009
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Herding dynamical weights to learn
M. Welling · 2009
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Recursive deep models for semantic compositionality over a sentiment treebank
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Active learning for convolutional neural networks: A core-set approach
O. Sener and S. Savarese · 2017
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A broad-coverage challenge corpus for sentence understanding through inference
A. Williams, N. Nangia, and S. R. Bowman · 2017
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Multiwoz–a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling
P. Budzianowski, T.-H. Wen, B.-H. Tseng, I. Casanueva, S. Ultes, O. Ramadan, and M. Gašić · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
N. Reimers and I. Gurevych · 2019
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Huggingface’s transformers: State-of-the-art natural language processing
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Hellaswag: Can a machine really finish your sentence?
R. Zellers, A. Holtzman, Y. Bisk, A. Farhadi, and Y. Choi · 2019
Cited alongside, same era.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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End-to-end object detection with transformers
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko · 2020
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Making pre-trained language models better few-shot learners
T. Gao, A. Fisch, and D. Chen · 2020
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Semantic evaluation for text-to-sql with distilled test suites
R. Zhong, T. Yu, and D. Klein · 2020
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Deepcore: A comprehensive library for coreset selection in deep learning
C. Guo, B. Zhao, and Y. Bai · 2022
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Prism: A rich class of parameterized submodular information measures for guided data subset selection
S. Kothawade, V. Kaushal, G. Ramakrishnan, J. Bilmes, and R. Iyer · 2022
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Chain-of-thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou, et al · 2022
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Z. Wu, Y. Wang, J. Ye, and L. Kong · 2022
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Moderate coreset: A universal method of data selection for real-world data-efficient deep learning
X. Xia, J. Liu, J. Yu, X. Shen, B. Han, and T. Liu · 2022
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GPT-Neo: Large scale autoregressive language modeling with meshtensorflow, Oct. 2021
S. Black, L. Gao, P. Wang, C. Leahy, and S. Biderman · 2021
Cited alongside, same era.
Submodular combinatorial information measures with applications in machine learning
R. Iyer, N. Khargoankar, J. Bilmes, and H. Asanani · 2021
Cited alongside, same era.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Y. Lu, M. Bartolo, A. Moore, S. Riedel, and P. Stenetorp · 2021
Cited alongside, same era.
Metaicl: Learning to learn in context
S. Min, M. Lewis, L. Zettlemoyer, and H. Hajishirzi · 2021
Cited alongside, same era.
Learning to retrieve prompts for in-context learning
O. Rubin, J. Herzig, and J. Berant · 2021
Cited alongside, same era.
Ptt: Point-track-transformer module for 3d single object tracking in point clouds
J. Shan, S. Zhou, Z. Fang, and Y. Cui · 2021
Cited alongside, same era.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
B. Wang and A. Komatsuzaki · 2021
Cited alongside, same era.
Ground-truth labels matter: A deeper look into input-label demonstrations
K. M. Yoo, J. Kim, H. J. Kim, H. Cho, H. Jo, S.-W. Lee, S.-G. Lee, and T. Kim · 2022
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Understanding in-context learning in transformers and llms by learning to learn discrete functions
S. Bhattamishra, A. Patel, P. Blunsom, and V. Kanade · 2023
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Active prompting with chain-of-thought for large language models
S. Diao, P. Wang, Y. Lin, and T. Zhang · 2023
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Finding supporting examples for in-context learning
X. Li and X. Qiu · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig · 2023
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Automatic prompt optimization with” gradient descent” and beam search
R. Pryzant, D. Iter, J. Li, Y. T. Lee, C. Zhu, and M. Zeng · 2023
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Selective annotation makes language models better few-shot learners
H. Su, J. Kasai, C. H. Wu, W. Shi, T. Wang, J. Xin, R. Zhang, M. Ostendorf, L. Zettlemoyer, N. A. Smith, et al · 2023
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Less is more: Fewer interpretable region via submodular subset selection
R. Chen, H. Zhang, S. Liang, J. Li, and X. Cao · 2024
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Ideal: Influence-driven selective annotations empower in-context learners in large language models
S. Zhang, X. Xia, Z. Wang, L.-H. Chen, J. Liu, Q. Wu, and T. Liu · 2024
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