Classic machine learning models like logistic regression built from scratch to distinguish between machine generated and human generated text.

Bag-of-words classifier for machine generated text detection

 - A baseline model trained on tf-idf vector representation of text and a logistic regression to detect WebText articles (online web pages) from text generated using GPT-2 models.

 - Study of different sizes of GPT-2 models indicated that models having a large number of parameters generated text somewhat similar to humans.

 - Text generated from Classifiers built with nucleus sampling are hard to detect.

 - Fine tuning GPT-2 specifically to amazon product reviews generated texts that are human generated.


Comparison of Bag-of-words classifier to detect TGMs 

- Identifying the modelling choice of TGM from the text generated by TGM.

 - It is easy to identify the modelling choice of TGM compared to distinguishing between machine and human generated text.


Detecting Machine Configuration

 - Bag-of-words classifier
 - Detecting machine configuration


San Jose State University

Detectors are used to identify machine generated text from human generated text. Classification of detectors are based on

- Classifiers trained from scratch 
 - Zero-shot classifier 
 - Fine-tuning NLM 
 - Human-machine collaboration


TGM Classifiers or Detectors

Automatic Detection of Machine Generated Text: A Critical Survey
by Ganesh Jawahar, Muhammad Abdul-Mageed, Laks V.S. Lakshmanan
https://arxiv.org/pdf/2011.01314.pdf

Automatic Detection of Machine Generated Text: A Critical Survey


In this a pre trained TGM model is used to classify texts generated from its model or similar models.

 - Total log probability
 - Giant Language model Test Room (GLTR) tool


Zero-shot classifier for TGMs

Fine tuning pretrained models to detect text samples from its own model or other similar models .the fine tuned pretrained model doesn’t require further supervised example training.

 - GROVER detector
 - RoBERTa detector


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