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:this https URL

该研究报告通过使用不同类型的语言模型并利用数据增强技术，提出了一种结合方法，旨在改善南斯拉夫南方微方言上大规模语言模型（LLMs）的通识推理能力评估，通过实验证明数据增强技术能够显著提升开源型模型类别在三个南斯拉夫方言（Chakavian、Cherkano 和 Torlak）的测试数据集上的性能，突出了数据增强的实际效用和 LLMs 在处理非标准方言方面的潜力以推进低资源和方言环境下的自然语言理解。