An End-to-End Submodular Framework for Data-Efficient In-Context Learning

Lilly Kumari; Shengjie Wang; Arnav Das; Tianyi Zhou; Jeff Bilmes

doi:10.18653/v1/2024.findings-naacl.209

An End-to-End Submodular Framework for Data-Efficient In-Context Learning

Lilly Kumari, Shengjie Wang, Arnav Das, Tianyi Zhou, Jeff Bilmes

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Anthology ID:: 2024.findings-naacl.209
Volume:: Findings of the Association for Computational Linguistics: NAACL 2024
Month:: June
Year:: 2024
Address:: Mexico City, Mexico
Editors:: Kevin Duh, Helena Gomez, Steven Bethard
Venue:: Findings
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 3293–3308
Language:
URL:: https://aclanthology.org/2024.findings-naacl.209/
DOI:: 10.18653/v1/2024.findings-naacl.209
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Cite (ACL):: Lilly Kumari, Shengjie Wang, Arnav Das, Tianyi Zhou, and Jeff Bilmes. 2024. An End-to-End Submodular Framework for Data-Efficient In-Context Learning. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3293–3308, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):: An End-to-End Submodular Framework for Data-Efficient In-Context Learning (Kumari et al., Findings 2024)
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PDF:: https://aclanthology.org/2024.findings-naacl.209.pdf
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@inproceedings{kumari-etal-2024-end,
    title = "An End-to-End Submodular Framework for Data-Efficient In-Context Learning",
    author = "Kumari, Lilly  and
      Wang, Shengjie  and
      Das, Arnav  and
      Zhou, Tianyi  and
      Bilmes, Jeff",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-naacl.209/",
    doi = "10.18653/v1/2024.findings-naacl.209",
    pages = "3293--3308"
}

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%0 Conference Proceedings
%T An End-to-End Submodular Framework for Data-Efficient In-Context Learning
%A Kumari, Lilly
%A Wang, Shengjie
%A Das, Arnav
%A Zhou, Tianyi
%A Bilmes, Jeff
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F kumari-etal-2024-end
%R 10.18653/v1/2024.findings-naacl.209
%U https://aclanthology.org/2024.findings-naacl.209/
%U https://doi.org/10.18653/v1/2024.findings-naacl.209
%P 3293-3308

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Markdown (Informal)

[An End-to-End Submodular Framework for Data-Efficient In-Context Learning](https://aclanthology.org/2024.findings-naacl.209/) (Kumari et al., Findings 2024)

An End-to-End Submodular Framework for Data-Efficient In-Context Learning (Kumari et al., Findings 2024)

ACL

Lilly Kumari, Shengjie Wang, Arnav Das, Tianyi Zhou, and Jeff Bilmes. 2024. An End-to-End Submodular Framework for Data-Efficient In-Context Learning. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3293–3308, Mexico City, Mexico. Association for Computational Linguistics.