ChatGPT Overview
The ability to recognize and test ChatGPT is becoming increasingly important to ensure the authenticity and reliability of digital content. Because AI chatbots such as ChatGPT have become an integral part of it. They offer innovative solutions for everyday work, private life and education. But with progress comes the challenge: How can we ensure that we correctly identify the origins of the information we find online?
In this article, you'll learn everything about recognizing and testing ChatGPT-generated texts using cutting-edge tools such as AI Classifier and AI Detectors.
Basics of AI chatbots
AI chatbots like ChatGPT work with an advanced type of artificial intelligence, which specializes in understanding and generating texts.
This AI, called Transformer, has basically “read” and analyzed a huge amount of text from the Internet and books. In doing so, she learned how words and phrases are normally used to express certain things.
When you ask ChatGPT a question or ask for text, it uses that knowledge to understand what you want and come up with an appropriate answer.
It looks at what it's learned about language, finds the best words and phrases for your request, and puts them together in such a way that you get an answer that sounds natural and human.
And that is the challenge: The AI texts are so difficult to recognize because they were generated on the basis of “real” texts.
In addition to ChatGPT, there are also a few other popular chatbots. You can find a comparison here: The best ChatGPT alternatives
How does ChatGPT write texts?
When ChatGPT generates text, it goes through the following steps:
- Understanding the input: First, it analyzes user input to understand the context and intent behind the request.
- Search for patterns: It then searches for similar patterns or contexts in its trained model to generate a suitable answer.
- Generate the response: Using learned language models and patterns, ChatGPT generates an answer that is based on input and is similar in style and content to trained data.
- optimization: Finally, the model adapts the answer for coherence, relevance, and naturalness to make it as human-like as possible.
Of course, this is only a superficial summary. You can find more information in our comprehensive article: How does ChatGPT work?
Why is ChatGPT so hard to prove?
The traceability of AI texts from ChatGPT and other chatbots is a demanding task that involves both technical and ethical challenges.
This is primarily due to three main factors: the system's high complexity and ability to learn, its distinctive understanding of context and the linguistic fluency of its answers.
Through extensive training with a wide range of textual data, ChatGPT has learned to imitate a variety of writing styles, keys, and even technical jargon.
This versatility makes his answers incredibly human-like and difficult to distinguish from real human texts.
In addition, ChatGPT is able to deeply understand the context of a request and, based on this, generate answers that are not only relevant but also provide a coherent continuation of the given topic.
Another aspect that sets ChatGPT apart from previous AI models is the linguistic fluency of its answers. The texts flow naturally and are free from the typical errors or inconsistencies that you might expect from machine-generated texts.
This combination of comprehension, adaptability, and language fluency makes ChatGPT a powerful tool for text generation. At the same time, however, it makes it much more difficult to distinguish between texts produced by AI and texts written by humans.
Proving AI texts: various methods
Text pattern analysis
This method focuses on identifying patterns intexts that typically occur in machine-generated content. A classic example oftext pattern analysis is the search for repetitions or noticeable redundanciesin the text, which can occur more frequently in AI-generated content than inhuman-written texts.
Behavioralanalysis
Here, writing behavior and interaction patterns are examined, which could indicate machinegeneration. This includes analyzing the speed and pattern with which content iscreated.
Example: You run an online forum and notice that posts from a specific useraccount always appear within 10 to 20 seconds of a question, no matter howcomplex the question is. The answers are correct in terms of content, butsometimes slightly off the core of the question or have a certain superficialnature that would probably have been avoided if examined more deeply.
Metadataanalysis
Checking themetadata associated with texts can provide clues as to their origin. Metadatacan include information about the author, when it was created, and the toolsused.
Example: You receive a new research paper that is being considered forpublication. The research paper has several suspicious features in itsmetadata: an author name with no verifiable academic history and a remarkablyfast and uninterrupted turnaround time. Taken together, these factors raise thesuspicion that the document may have been generated using AI rather thanwritten by a real researcher.
Technical instruments
Special recognition tools, such as the OpenAI AI Text Classifier, aim todifferentiate between human-written and AI-generated texts. These tools oftenuse complex algorithms to assess the likelihood of an AI origin.
Machinelearning
ML models can betrained on data sets with known human-written and AI-generated texts. Thesemodels learn to recognize differentiators and can be used to identify AI texts.
Stilometricanalysis
By analyzing the writing style, syntax, and other textual features, conclusionscan be drawn about the source of the text. Styliometric analysis can revealsubtle differences that aren't obvious to human readers.
Example: A term paper has a noticeably more complex sentence structure and amore precise choice of words than previous papers by the same student. Inaddition, the work uses specific technical jargon that is not present in thestudent's reference texts. The stilometric analysis shows that these characteristicsdiffer significantly from the student's individual writing habits and insteadhave similarities with typical patterns found in AI-generated texts.
Complexityand coherence analysis
Checking howcomplex and coherent a text is can also provide clues. AI-generated textssometimes have anomalies in these areas that distinguish them fromhuman-written texts.
Example: A submitted article on a complex scientific topic has a consistentlyhigh sentence complexity, but this does not always contribute to understandingthe content. Some sections jump from one topic to the next without a cleartransition, which affects the coherence of the overall text. Although thearticle is technically accurate, the way information is presented sometimesseems artificial and not entirely natural.
Plagiarismdetection
Plagiarismdetection tools can help identify repeated or copied content that could beindicative of AI-generated texts.
Example: A blog post has 70% agreement with various sources on the Internet.The tool highlights specific sections that have almost identical wording tocontent that already exists on other websites. This suggests that the post mayhave been created using an AI generator that paraphrases existing content togenerate a “new” post without offering real originality or unique insights.
Combined methods
A combination ofthe methods mentioned above often provides the most effective solution forrecognizing AI texts. By using different approaches, the strengths of onemethod can compensate for the weaknesses of another.
Detect ChatGPT: Which AI detectors are the best?
- AI Text Classifier
The”AI Text Classifier“OpenAI is a tool that aims to determine whether a text was written by a human or an AI. It analyses texts based on OpenAI training data and provides an assessment of whether the text is likely to be AI-generated. Although the tool is able to recognize a lot of AI-generated content, it has limitations, including the minimum number of characters required and some unreliability for texts that are not in English or written by children. -
- GLTR
GLTR (Giant Language Model Test Room) is a tool developed by Harvard University and the MIT-IBM Watson AI Lab, for recognizing AI-generated texts. It visualizes the predictive probabilities of every word in a text to reveal anomalies typical of AI-generated content. By analyzing text patterns, GLTR helps users determine whether a text was likely generated by an AI such as GPT (Generative Pre-trained Transformer). -
- Grover Detector
The Grover Detector is a specialized tool for recognizing texts generated by Grover, an advanced AI model for text creation. It analyses texts for specific patterns and idiosyncrasies that are typical of content generated by Grover, and thus helps to determine whether a text is likely to come from this AI. -
- Originality.ai
Originality.ai is a tool that offers both AI text recognition and plagiarism checking. It is characterized by the ability to effectively understand obfuscation tactics and reliably identify AI-generated texts. With an easy-to-use interface and the ability to run both checks at the same time, Originality.ai saves time and effort. It is particularly useful for content creators and educational institutions who want to ensure the originality and authenticity of texts. -
- ZeroGPT
ZeroGPT is a tool for recognizing AI-generated texts that requires a high accuracy rate for English texts, based on the analysis of millions of articles and texts. It offers a user-friendly interface, data protection by not saving the analyzed texts and focuses exclusively on recognition without offering correction or editing functions. There may occasionally be false positive or negative results.
Can AI detectors be trusted?
There is still no way to reliably recognize AI texts. AI detectors can be useful tools for identifying AI-generated texts, but their reliability varies greatly.
The effectiveness depends on several factors, such as the complexity of the text to be recognized, the quality and amount of training of the detector, and the specific AI that generated the text.
While advanced detectors can provide correct results in many cases, there is always the possibility of false alarms or false negatives. In the educational context in particular, misunderstandings and disputes occur more and more frequently, which end up in court more and more often.
Can you use ChatGPT at school or university?
The use of ChatGPT in schools or universities depends on the guidelines of the respective educational institution.
Some institutions allow the use of AI tools such as ChatGPT to support the learning process, research, or brainstorming, as long as the sources are cited.
Others are critical of the use, particularly when it comes to preparing term papers or examination papers, as the focus should be on students' own efforts.
Not sure if you should sign up for ChatGPT? So you can Try ChatGPT without signing in.
How can teachers recognize AI texts?
Teachers find it difficult to clearly recognize and verify texts from ChatGPT and other AI chatbots. In particular, the use of AI detectors and their effectiveness is highly controversial.
Styliometric analysis, complexity and coherence analysis, and plagiarism detection are methods of AI detection that make the most sense for teachers. However, particularly when using third-party tools for analysis, you should also consider the Data protection with AI tools be thought of.
It is also beneficial to guide pupils and students to critically reflect and use AI tools ethically.
You can also find out more about data protection in our course: ChatGPT, AI, and Law
How can AI texts be made recognizable in the future?
In the future, AI texts could be made recognizable through the development of advanced recognition algorithms, the introduction of digital watermarks or specific patterns that only occur in AI-generated texts, and through improved training data for recognition tools.
Improved tools could increase accuracy when distinguishing between human and AI texts. Ethics and education play a key role in preventing abuse and promoting informed use. Regulation and development of open-source tools, as well as community engagement, could jointly improve the transparency and traceability of AI-generated content.
