Data-Augmentation-Based Dialectal Adaptation for LLMs
Fahim Faisal Antonios Anastasopoulos
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