# To ChatGPT, or not to ChatGPT: That is the question!

arXiv: https://arxiv.org/abs/2304.01487 · HTML: https://mlsanity.com/paper/2304.01487/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [Regulating ChatGPT and other Large Generative AI Models](https://mlsanity.com/paper/2302.02337/) (2023)
- [ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text](https://mlsanity.com/paper/2301.13852/) (2023)
- [Stylometric Detection of AI-Generated Text in Twitter Timelines](https://mlsanity.com/paper/2303.03697/) (2023)
- [Artificial Text Detection via Examining the Topology of Attention Maps](https://mlsanity.com/paper/2109.04825/) (2021)
- [ChatGPT versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions Towards Knowledge Graph Chatbots](https://mlsanity.com/paper/2302.06466/) (2023)
- [Will ChatGPT get you caught? Rethinking of Plagiarism Detection](https://mlsanity.com/paper/2302.04335/) (2023)
- [Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature](https://mlsanity.com/paper/2302.06474/) (2023)

## Built on by (most cited first, 20 of 20)

- ["Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models](https://mlsanity.com/paper/2308.03825/) (2023)
- [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://mlsanity.com/paper/2305.18486/) (2023)
- [A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://mlsanity.com/paper/2305.11391/) (2023)
- [Testing of Detection Tools for AI-Generated Text](https://mlsanity.com/paper/2306.15666/) (2023)
- [Semantic Mirror Jailbreak: Genetic Algorithm Based Jailbreak Prompts Against Open-source LLMs](https://mlsanity.com/paper/2402.14872/) (2024)
- [Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices](https://mlsanity.com/paper/2403.12503/) (2024)
- [Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey](https://mlsanity.com/paper/2310.15264/) (2023)
- [PAPILLON: Efficient and Stealthy Fuzz Testing-Powered Jailbreaks for LLMs](https://mlsanity.com/paper/2409.14866/) (2024)
- [A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterization](https://mlsanity.com/paper/2403.01152/) (2024)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text](https://mlsanity.com/paper/2309.07689/) (2023)
- [An Empirical Study of AI Generated Text Detection Tools](https://mlsanity.com/paper/2310.01423/) (2023)
- [ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness](https://mlsanity.com/paper/2403.18121/) (2024)
- [Detecting The Corruption Of Online Questionnaires By Artificial Intelligence](https://mlsanity.com/paper/2308.07499/) (2023)
- [Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT](https://mlsanity.com/paper/2406.09056/) (2024)
- [Applying Ensemble Methods to Model-Agnostic Machine-Generated Text Detection](https://mlsanity.com/paper/2406.12570/) (2024)
- [Generation Z's Ability to Discriminate Between AI-generated and Human-Authored Text on Discord](https://mlsanity.com/paper/2401.04120/) (2023)
- [Attention Paper: How Generative AI Reshapes Digital Shadow Industry?](https://mlsanity.com/paper/2305.18346/) (2023)
- [Detection of news written by the ChatGPT through authorship attribution performed by a Bidirectional LSTM model](https://mlsanity.com/paper/2310.16685/) (2023)
- [Beyond Detection: Designing AI-Resilient Assessments with Automated Feedback Tool to Foster Critical Thinking](https://mlsanity.com/paper/2503.23622/) (2025)

## Abstract (arXiv)

ChatGPT has become a global sensation. As ChatGPT and other Large Language Models (LLMs) emerge, concerns of misusing them in various ways increase, such as disseminating fake news, plagiarism, manipulating public opinion, cheating, and fraud. Hence, distinguishing AI-generated from human-generated becomes increasingly essential. Researchers have proposed various detection methodologies, ranging from basic binary classifiers to more complex deep-learning models. Some detection techniques rely on statistical characteristics or syntactic patterns, while others incorporate semantic or contextual information to improve accuracy. The primary objective of this study is to provide a comprehensive and contemporary assessment of the most recent techniques in ChatGPT detection. Additionally, we evaluated other AI-generated text detection tools that do not specifically claim to detect ChatGPT-generated content to assess their performance in detecting ChatGPT-generated content. For our evaluation, we have curated a benchmark dataset consisting of prompts from ChatGPT and humans, including diverse questions from medical, open Q&A, and finance domains and user-generated responses from popular social networking platforms. The dataset serves as a reference to assess the performance of various techniques in detecting ChatGPT-generated content. Our evaluation results demonstrate that none of the existing methods can effectively detect ChatGPT-generated content.