Data-Augmentation-Based Dialectal Adaptation for LLMs
<|reference_start|>Data-Augmentation-Based Dialectal Adaptation for LLMs: This report presents GMUNLP's participation to the Dialect-Copa shared task at VarDial 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 (BERTi\'c) 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. Code:https://github.com/ffaisal93/dialect_copa<|reference_end|>
@article{faisal2024data-augmentation-based,
    title={Data-Augmentation-Based Dialectal Adaptation for LLMs},
    author={Fahim Faisal and Antonios Anastasopoulos},
    journal={arXiv preprint arXiv:2404.08092},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08092},
    primaryClass={cs.CL cs.AI}
}