Subasa - Adapting Language Models for Low-resourced Offensive Language Detection in Sinhala
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arXiv
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| Format: | Preprint |
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2025
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| author | Haturusinghe, Shanilka Weerasooriya, Tharindu Cyril Zampieri, Marcos Homan, Christopher M. Liyanage, S. R. |
| author_facet | Haturusinghe, Shanilka Weerasooriya, Tharindu Cyril Zampieri, Marcos Homan, Christopher M. Liyanage, S. R. |
| contents | Accurate detection of offensive language is essential for a number of applications related to social media safety. There is a sharp contrast in performance in this task between low and high-resource languages. In this paper, we adapt fine-tuning strategies that have not been previously explored for Sinhala in the downstream task of offensive language detection. Using this approach, we introduce four models: "Subasa-XLM-R", which incorporates an intermediate Pre-Finetuning step using Masked Rationale Prediction. Two variants of "Subasa-Llama" and "Subasa-Mistral", are fine-tuned versions of Llama (3.2) and Mistral (v0.3), respectively, with a task-specific strategy. We evaluate our models on the SOLD benchmark dataset for Sinhala offensive language detection. All our models outperform existing baselines. Subasa-XLM-R achieves the highest Macro F1 score (0.84) surpassing state-of-the-art large language models like GPT-4o when evaluated on the same SOLD benchmark dataset under zero-shot settings. The models and code are publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02178 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Subasa - Adapting Language Models for Low-resourced Offensive Language Detection in Sinhala Haturusinghe, Shanilka Weerasooriya, Tharindu Cyril Zampieri, Marcos Homan, Christopher M. Liyanage, S. R. Computation and Language Accurate detection of offensive language is essential for a number of applications related to social media safety. There is a sharp contrast in performance in this task between low and high-resource languages. In this paper, we adapt fine-tuning strategies that have not been previously explored for Sinhala in the downstream task of offensive language detection. Using this approach, we introduce four models: "Subasa-XLM-R", which incorporates an intermediate Pre-Finetuning step using Masked Rationale Prediction. Two variants of "Subasa-Llama" and "Subasa-Mistral", are fine-tuned versions of Llama (3.2) and Mistral (v0.3), respectively, with a task-specific strategy. We evaluate our models on the SOLD benchmark dataset for Sinhala offensive language detection. All our models outperform existing baselines. Subasa-XLM-R achieves the highest Macro F1 score (0.84) surpassing state-of-the-art large language models like GPT-4o when evaluated on the same SOLD benchmark dataset under zero-shot settings. The models and code are publicly available. |
| title | Subasa - Adapting Language Models for Low-resourced Offensive Language Detection in Sinhala |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2504.02178 |