## 2 Related Work
### Detection Models  
[PERSON] et al. [14] developed the Human-ChatGPT Comparison Corpus (HC3) to distinguish between texts written by ChatGPT and humans. They collected human-written question-answer pairs from websites such as Reddit and Wikipedia and then generated ChatGPT answers to these. They then developed three models for detection purposes. The first was a logistic regression model trained on GLTR (Giant Language Model Test Room) Test 2, which uses GPT2 on the backend to detect AI-generated text from human-written text. The second was a RoBERTa-based classifier model and the third was a RoBERTa-based question-answer model. They found that the RoBERTa-based detectors were better than GLTR in the detection task with an average F1-score of 98.78%. They also found the RoBERTa-QA-based model to be more effective than the RoBERTa classifier model with an F1-score of 95.30%. In another similar study, [PERSON] et al. [15] used text created from nine sources, with one source being human written text (news articles) and the remaining eight being the following language models: CTRL, GPT, GPT2, GROVER, XLM, XLNET, PPLM, and FAIR. Their definitions are as follows. CTRL [37]: Conditional Transformer Language Model For Controllable Generation, a language model that supports adding control codes that specify domain, subdomain, entities, etc., to guide the text generation process ([[https://github.com/salesforce/ctrl](https://github.com/salesforce/ctrl)]([https://github.com/salesforce/ctrl](https://github.com/salesforce/ctrl))) (accessed on 19 January 2025). GPT [38] and GPT2 [1] (OpenAI models): GPT was trained and modeled with the goal of predicting the next token, whereby 345 million parameters were used; GPT2 was trained using 774 million parameters. GROVER [9]: A model trained to generate political news. XLM [39]: A generative language model that was trained for the task of cross-lingual classification. XLNET [40]: This model introduced bidirectional contexts and outperformed BERT on 20 tasks. PPLM [41]: This model improved upon GPT2 by combining the medium model with bag-of-words models. FAIR [42]: Created by Facebook, it has three language models of English, Russian, and German. The authors attempted to solve three problems: given two texts, predict if they were generated by the same method; given a text, predict if it was human-generated; and given a text, predict the method that was used to generate it. They found that texts generated by most models are significantly different from human text (in terms of readability and psycholinguistic metrics--LIWC [43]). They found that it can be fairly easily identified whether or not the text was generated by the same method (F1-0.78-0.98). For human vs. AI prediction, GPT2 was the hardest to distinguish. However, a RoBERTa-tuned model (the authors fine-tuned the RoBERTa [44] model with 20% of their data by adding a classification layer on top of it) was able to make accurate predictions (F1-0.98), and a Random Forest model was able to accurately predict (F1-0.90) which model the text was generated from. Studying GPT-2 specifically, [PERSON] et al. [16] released several baselines for potential GPT-2 detectors. The first was a simple logistic regression classifier on TF-IDF uni- and bi-gram features. This classifier was able to reach accuracies ranging from 88% on the 124-million GPT-2 model and 74% on the 1.5-billion-parameter model (93-97% when Top-K was constrained to 40). The second was a \"zero-shot\" baseline that used the GPT-2 model itself to predict if the total probability of generating the text in question is higher than a certain threshold. This approach had accuracies of 83-85%. However, the accuracy dropped to 76% when the text was generated using a fine-tuned model. The third baseline is a RoBERTa-based classifier that had an accuracy of 95%. [PERSON] et al. [9] similarly presentsa neural text generation model, called GROVER, that can generate a news article given a headline.