Small Language Models Are Good Too: An Empirical Study of Zero-Shot Classification

Pierre Lepagnol, Thomas Gerald, Sahar Ghannay, Christophe Servan, Sophie Rosset

Abstract
This study is part of the debate on the efficiency of large versus small language models for text classification by prompting. We assess the performance of small language models in zero-shot text classification, challenging the prevailing dominance of large models. Across 15 datasets, our investigation benchmarks language models from 77M to 40B parameters using different architectures and scoring functions. Our findings reveal that small models can effectively classify texts, getting on par with or surpassing their larger counterparts. We developed and shared a comprehensive open-source repository that encapsulates our methodologies. This research underscores the notion that bigger isn’t always better, suggesting that resource-efficient small models may offer viable solutions for specific data classification challenges.
Anthology ID:
2024.lrec-main.1299
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
14923–14936
Language:
URL:
https://aclanthology.org/2024.lrec-main.1299
DOI:
Bibkey:
Cite (ACL):
Pierre Lepagnol, Thomas Gerald, Sahar Ghannay, Christophe Servan, and Sophie Rosset. 2024. Small Language Models Are Good Too: An Empirical Study of Zero-Shot Classification. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 14923–14936, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Small Language Models Are Good Too: An Empirical Study of Zero-Shot Classification (Lepagnol et al., LREC-COLING 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1299.pdf
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