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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2411.19869 |
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| _version_ | 1866917852228878336 |
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| author | Almeida, Leonardo Rodrigues, Pedro Magalhães, Diogo Pinho, Armando J. Pratas, Diogo |
| author_facet | Almeida, Leonardo Rodrigues, Pedro Magalhães, Diogo Pinho, Armando J. Pratas, Diogo |
| contents | This paper introduces AIDetx, a novel method for detecting machine-generated text using data compression techniques. Traditional approaches, such as deep learning classifiers, often suffer from high computational costs and limited interpretability. To address these limitations, we propose a compression-based classification framework that leverages finite-context models (FCMs). AIDetx constructs distinct compression models for human-written and AI-generated text, classifying new inputs based on which model achieves a higher compression ratio. We evaluated AIDetx on two benchmark datasets, achieving F1 scores exceeding 97% and 99%, respectively, highlighting its high accuracy. Compared to current methods, such as large language models (LLMs), AIDetx offers a more interpretable and computationally efficient solution, significantly reducing both training time and hardware requirements (e.g., no GPUs needed). The full implementation is publicly available at https://github.com/AIDetx/AIDetx. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19869 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AIDetx: a compression-based method for identification of machine-learning generated text Almeida, Leonardo Rodrigues, Pedro Magalhães, Diogo Pinho, Armando J. Pratas, Diogo Computation and Language Machine Learning This paper introduces AIDetx, a novel method for detecting machine-generated text using data compression techniques. Traditional approaches, such as deep learning classifiers, often suffer from high computational costs and limited interpretability. To address these limitations, we propose a compression-based classification framework that leverages finite-context models (FCMs). AIDetx constructs distinct compression models for human-written and AI-generated text, classifying new inputs based on which model achieves a higher compression ratio. We evaluated AIDetx on two benchmark datasets, achieving F1 scores exceeding 97% and 99%, respectively, highlighting its high accuracy. Compared to current methods, such as large language models (LLMs), AIDetx offers a more interpretable and computationally efficient solution, significantly reducing both training time and hardware requirements (e.g., no GPUs needed). The full implementation is publicly available at https://github.com/AIDetx/AIDetx. |
| title | AIDetx: a compression-based method for identification of machine-learning generated text |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2411.19869 |