Comparing LLM prompting with Cross-lingual transfer performance on Indigenous and Low-resource Brazilian Languages
David Ifeoluwa Adelani, A. Seza Doğruöz, André Coneglian, Atul Kr. Ojha
Abstract
Large Language Models are transforming NLP for a lot of tasks. However, how LLMs perform NLP tasks for LRLs is less explored. In alliance with the theme track of the NAACL’24, we focus on 12 low-resource languages (LRLs) from Brazil, 2 LRLs from Africa and 2 high-resource languages (HRLs) (e.g., English and Brazilian Portuguese). Our results indicate that the LLMs perform worse for the labeling of LRLs in comparison to HRLs in general. We explain the reasons behind this failure and provide an error analyses through examples from 2 Brazilian LRLs.- Anthology ID:
- 2024.americasnlp-1.5
- Volume:
- Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Manuel Mager, Abteen Ebrahimi, Shruti Rijhwani, Arturo Oncevay, Luis Chiruzzo, Robert Pugh, Katharina von der Wense
- Venues:
- AmericasNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 34–41
- Language:
- URL:
- https://aclanthology.org/2024.americasnlp-1.5
- DOI:
- Cite (ACL):
- David Ifeoluwa Adelani, A. Seza Doğruöz, André Coneglian, and Atul Kr. Ojha. 2024. Comparing LLM prompting with Cross-lingual transfer performance on Indigenous and Low-resource Brazilian Languages. In Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024), pages 34–41, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Comparing LLM prompting with Cross-lingual transfer performance on Indigenous and Low-resource Brazilian Languages (Adelani et al., AmericasNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.americasnlp-1.5.pdf