Abstract
This report presents gmnlp’s participation to the Dialect-Copa shared task at VarDial 2024 (Chifu et al., 2024), which focuses on evaluating the commonsense reasoning capabilities of large language models (LLMs) on South Slavic micro-dialects. The task aims to assess how well LLMs can handle non-standard dialectal varieties, as their performance on standard languages is already well-established. We propose an approach that combines the strengths of different types of language models and leverages data augmentation techniques to improve task performance on three South Slavic dialects: Chakavian, Cherkano, and Torlak. We conduct experiments using a language-family-focused encoder-based model (BERTić) and a domain-agnostic multilingual model (AYA-101). Our results demonstrate that the proposed data augmentation techniques lead to substantial performance gains across all three test datasets in the open-source model category. This work highlights the practical utility of data augmentation and the potential of LLMs in handling non-standard dialectal varieties, contributing to the broader goal of advancing natural language understanding in low-resource and dialectal settings.- Anthology ID:
- 2024.vardial-1.17
- Volume:
- Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Yves Scherrer, Tommi Jauhiainen, Nikola Ljubešić, Marcos Zampieri, Preslav Nakov, Jörg Tiedemann
- Venues:
- VarDial | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 197–208
- Language:
- URL:
- https://aclanthology.org/2024.vardial-1.17
- DOI:
- Cite (ACL):
- Fahim Faisal and Antonios Anastasopoulos. 2024. Data-Augmentation-Based Dialectal Adaptation for LLMs. In Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024), pages 197–208, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Data-Augmentation-Based Dialectal Adaptation for LLMs (Faisal & Anastasopoulos, VarDial-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.vardial-1.17.pdf