Comparing Prompt-Based and Standard Fine-Tuning for Urdu Text Classification
Faizad Ullah, Ubaid Azam, Ali Faheem, Faisal Kamiran, Asim Karim
Abstract
Recent advancements in natural language processing have demonstrated the efficacy of pre-trained language models for various downstream tasks through prompt-based fine-tuning. In contrast to standard fine-tuning, which relies solely on labeled examples, prompt-based fine-tuning combines a few labeled examples (few shot) with guidance through prompts tailored for the specific language and task. For low-resource languages, where labeled examples are limited, prompt-based fine-tuning appears to be a promising alternative. In this paper, we compare prompt-based and standard fine-tuning for the popular task of text classification in Urdu and Roman Urdu languages. We conduct experiments using five datasets, covering different domains, and pre-trained multilingual transformers. The results reveal that significant improvement of up to 13% in accuracy is achieved by prompt-based fine-tuning over standard fine-tuning approaches. This suggests the potential of prompt-based fine-tuning as a valuable approach for low-resource languages with limited labeled data.- Anthology ID:
- 2023.findings-emnlp.449
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6747–6754
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.449
- DOI:
- 10.18653/v1/2023.findings-emnlp.449
- Cite (ACL):
- Faizad Ullah, Ubaid Azam, Ali Faheem, Faisal Kamiran, and Asim Karim. 2023. Comparing Prompt-Based and Standard Fine-Tuning for Urdu Text Classification. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6747–6754, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Comparing Prompt-Based and Standard Fine-Tuning for Urdu Text Classification (Ullah et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.findings-emnlp.449.pdf