Summary of Aidetx: a Compression-based Method For Identification Of Machine-learning Generated Text, by Leonardo Almeida et al.
AIDetx: a compression-based method for identification of machine-learning generated text
by Leonardo Almeida, Pedro Rodrigues, Diogo Magalhães, Armando J. Pinho, Diogo Pratas
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method, AIDetx, uses data compression techniques to detect machine-generated text. Unlike traditional deep learning-based approaches, AIDetx leverages finite-context models (FCMs) and constructs distinct compression models for human-written and AI-generated text. The framework classifies new inputs based on which model achieves a higher compression ratio, demonstrating high accuracy with F1 scores exceeding 97% and 99% on two benchmark datasets. Compared to large language models (LLMs), AIDetx offers a more interpretable and computationally efficient solution, reducing training time and hardware requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AIDetx is a new way to tell real writing from computer-generated text. It uses special techniques called data compression to figure out which kind of writing it’s looking at. This helps make the process faster and easier than old methods that use super powerful computers. The results are very good, with accuracy over 97% on two big datasets. What’s cool is that this method doesn’t need those super powerful computers, so it can be used anywhere. |
Keywords
» Artificial intelligence » Deep learning
