Automating Violence Detection and Categorization from Ancient Texts
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912269851426816 |
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| author | Abdelhalim, Alhassan Regneri, Michaela |
| author_facet | Abdelhalim, Alhassan Regneri, Michaela |
| contents | Violence descriptions in literature offer valuable insights for a wide range of research in the humanities. For historians, depictions of violence are of special interest for analyzing the societal dynamics surrounding large wars and individual conflicts of influential people. Harvesting data for violence research manually is laborious and time-consuming. This study is the first one to evaluate the effectiveness of large language models (LLMs) in identifying violence in ancient texts and categorizing it across multiple dimensions. Our experiments identify LLMs as a valuable tool to scale up the accurate analysis of historical texts and show the effect of fine-tuning and data augmentation, yielding an F1-score of up to 0.93 for violence detection and 0.86 for fine-grained violence categorization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08192 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automating Violence Detection and Categorization from Ancient Texts Abdelhalim, Alhassan Regneri, Michaela Computation and Language Digital Libraries Machine Learning Violence descriptions in literature offer valuable insights for a wide range of research in the humanities. For historians, depictions of violence are of special interest for analyzing the societal dynamics surrounding large wars and individual conflicts of influential people. Harvesting data for violence research manually is laborious and time-consuming. This study is the first one to evaluate the effectiveness of large language models (LLMs) in identifying violence in ancient texts and categorizing it across multiple dimensions. Our experiments identify LLMs as a valuable tool to scale up the accurate analysis of historical texts and show the effect of fine-tuning and data augmentation, yielding an F1-score of up to 0.93 for violence detection and 0.86 for fine-grained violence categorization. |
| title | Automating Violence Detection and Categorization from Ancient Texts |
| topic | Computation and Language Digital Libraries Machine Learning |
| url | https://arxiv.org/abs/2503.08192 |