Summary of Ai-generated Text Detection and Classification Based on Bert Deep Learning Algorithm, by Hao Wang et al.
AI-Generated Text Detection and Classification Based on BERT Deep Learning Algorithm
by Hao Wang, Jianwei Li, Zhengyu Li
First submitted to arxiv on: 26 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 paper presents a novel AI-generated text detection model based on the BERT algorithm, which offers innovative solutions to address related challenges. The model is developed by processing the text through various steps such as converting to lowercase, word splitting, and removing stop words. A dataset is divided into training and test sets (60:40) to evaluate the model’s performance during training. The results show that the accuracy increases from 94.78% to 99.72%, while the loss value decreases from 0.261 to 0.021, indicating high accuracy in detecting AI-generated text. The study also explores the model’s generalization ability, revealing similar accuracy and loss values for both training and test sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops an AI-generated text detection model based on BERT that detects AI-generated text with high accuracy. It uses a series of steps to process the text, including converting it to lowercase and removing stop words. The model is trained and tested on a dataset, showing steady increases in accuracy from 94.78% to 99.72%. The study also compares the loss values during training, revealing that the model has good generalization ability. |
Keywords
» Artificial intelligence » Bert » Generalization
