Navigating prompt complexity for zero-shot classification: a study of large language models in computational social science

Mu, Y., Wu, B.P., Thorne, W. orcid.org/0000-0002-8947-6261 et al. (5 more authors) (2024) Navigating prompt complexity for zero-shot classification: a study of large language models in computational social science. In: Calzolari, N., Kan, M-Y., Hoste, V., Lenci, A., Sakti, S. and Xue, N., (eds.) Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), 20-25 May 2024, Torino, Italy. ELRA and ICCL , pp. 12074-12086. ISBN 978-2-493814-10-4

Abstract

Metadata

Item Type: Proceedings Paper
Authors/Creators:
Editors:
  • Calzolari, N.
  • Kan, M-Y.
  • Hoste, V.
  • Lenci, A.
  • Sakti, S.
  • Xue, N.
Copyright, Publisher and Additional Information:

© 2024 ELRA Language Resource Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-commercial Licence (https://creativecommons.org/licenses/by-nc/4.0/).

Keywords: Large Language Model; Computational Social Science; Prompt Complexity
Dates:
  • Published: May 2024
  • Published (online): May 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 13 Feb 2025 14:42
Last Modified: 14 Feb 2025 09:46
Published Version: https://aclanthology.org/2024.lrec-main.1055/
Status: Published
Publisher: ELRA and ICCL
Refereed: Yes
Related URLs:
Open Archives Initiative ID (OAI ID):

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