An End-to-End Submodular Framework for Data-Efficient In-Context Learning
Lilly Kumari, Shengjie Wang, Arnav Das, Tianyi Zhou, Jeff Bilmes
- 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://preview.aclanthology.org/add-emnlp-2024-awards/2024.findings-naacl.209/
- DOI:
- 10.18653/v1/2024.findings-naacl.209
- 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)
- PDF:
- https://preview.aclanthology.org/add-emnlp-2024-awards/2024.findings-naacl.209.pdf