Data-Augmentation-Based Dialectal Adaptation for LLMs
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916202469654528 |
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| author | Faisal, Fahim Anastasopoulos, Antonios |
| author_facet | Faisal, Fahim Anastasopoulos, Antonios |
| contents | 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 (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. Code:https://github.com/ffaisal93/dialect_copa |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08092 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Augmentation-Based Dialectal Adaptation for LLMs Faisal, Fahim Anastasopoulos, Antonios Computation and Language Artificial Intelligence 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 (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. Code:https://github.com/ffaisal93/dialect_copa |
| title | Data-Augmentation-Based Dialectal Adaptation for LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2404.08092 |