Who Wrote This? Identifying Machine vs Human-Generated Text in Hausa
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912278444507136 |
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| author | Sani, Babangida Soy, Aakansha Imam, Sukairaj Hafiz Mustapha, Ahmad Aliyu, Lukman Jibril Abdulmumin, Idris Ahmad, Ibrahim Said Muhammad, Shamsuddeen Hassan |
| author_facet | Sani, Babangida Soy, Aakansha Imam, Sukairaj Hafiz Mustapha, Ahmad Aliyu, Lukman Jibril Abdulmumin, Idris Ahmad, Ibrahim Said Muhammad, Shamsuddeen Hassan |
| contents | The advancement of large language models (LLMs) has allowed them to be proficient in various tasks, including content generation. However, their unregulated usage can lead to malicious activities such as plagiarism and generating and spreading fake news, especially for low-resource languages. Most existing machine-generated text detectors are trained on high-resource languages like English, French, etc. In this study, we developed the first large-scale detector that can distinguish between human- and machine-generated content in Hausa. We scrapped seven Hausa-language media outlets for the human-generated text and the Gemini-2.0 flash model to automatically generate the corresponding Hausa-language articles based on the human-generated article headlines. We fine-tuned four pre-trained Afri-centric models (AfriTeVa, AfriBERTa, AfroXLMR, and AfroXLMR-76L) on the resulting dataset and assessed their performance using accuracy and F1-score metrics. AfroXLMR achieved the highest performance with an accuracy of 99.23% and an F1 score of 99.21%, demonstrating its effectiveness for Hausa text detection. Our dataset is made publicly available to enable further research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13101 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Who Wrote This? Identifying Machine vs Human-Generated Text in Hausa Sani, Babangida Soy, Aakansha Imam, Sukairaj Hafiz Mustapha, Ahmad Aliyu, Lukman Jibril Abdulmumin, Idris Ahmad, Ibrahim Said Muhammad, Shamsuddeen Hassan Computation and Language The advancement of large language models (LLMs) has allowed them to be proficient in various tasks, including content generation. However, their unregulated usage can lead to malicious activities such as plagiarism and generating and spreading fake news, especially for low-resource languages. Most existing machine-generated text detectors are trained on high-resource languages like English, French, etc. In this study, we developed the first large-scale detector that can distinguish between human- and machine-generated content in Hausa. We scrapped seven Hausa-language media outlets for the human-generated text and the Gemini-2.0 flash model to automatically generate the corresponding Hausa-language articles based on the human-generated article headlines. We fine-tuned four pre-trained Afri-centric models (AfriTeVa, AfriBERTa, AfroXLMR, and AfroXLMR-76L) on the resulting dataset and assessed their performance using accuracy and F1-score metrics. AfroXLMR achieved the highest performance with an accuracy of 99.23% and an F1 score of 99.21%, demonstrating its effectiveness for Hausa text detection. Our dataset is made publicly available to enable further research. |
| title | Who Wrote This? Identifying Machine vs Human-Generated Text in Hausa |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.13101 |