Summary of Detecting Ai Generated Text Based on Nlp and Machine Learning Approaches, by Nuzhat Prova
Detecting AI Generated Text Based on NLP and Machine Learning Approaches
by Nuzhat Prova
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 A recent breakthrough in natural language processing (NLP) has sparked concerns about artificial intelligence (AI) models generating writing that mimics human-written form. This paper addresses this issue by proposing an accurate AI detector model to distinguish between electronically produced text and human-written text. The approach employs machine learning methods, including XGB Classifier, SVM, and BERT architecture deep learning models. The study demonstrates the effectiveness of these methods, particularly BERT, which outperforms previous models in identifying AI-generated text. The research also provides a comprehensive analysis of the current state of AI-generated text identification, highlighting its potential applications and societal implications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers have been working on detecting artificially generated text to ensure ethical and legal standards are met. A new study proposes an accurate model that can tell human-written text from AI-generated text. The model uses machine learning techniques like XGB Classifier, SVM, and BERT architecture. It’s shown to be very good at distinguishing between the two types of text. The research also looks at how well other models have done in the past and what this means for society. |
Keywords
» Artificial intelligence » Bert » Deep learning » Machine learning » Natural language processing » Nlp
