OpenAI Text Classifier Fails to Detect ChatGPT Text | ninjasquad
OpenAI provides an Online Text Classifier Tool to detect if a text (min 1000 characters, 150 to 250 words) is likely to be generated by AI such as ChatGPT or Notion AI.
However, it seems not very accurate.
Both of the following two examples are generated by ChatGPT however they are being detected as “Unclear” and “Very Unlikely”.
Of course, I believe the accuracy will be improving, and you can feed back the incorrect result to the classifier (supervised, Reinforcement learning) – so a better machine learning model.
However, it is correct to say that: “The classifier considers the text to be likely AI-generated.”
OpenAI’s Text Classifier is a powerful tool for natural language processing (NLP). It can be used to classify text into different categories, such as sentiment analysis, topic classification, and more. However, recently it has been found that the OpenAI Text Classifier fails to detect ChatGPT text.
ChatGPT is a new type of text-based chatbot developed by OpenAI. It uses a deep learning model to generate responses to user input in real time. This makes it possible for users to have conversations with the chatbot in natural language. However, the OpenAI Text Classifier is not able to detect ChatGPT text correctly. This means that when the Text Classifier is used to classify text into different categories, it will not be able to accurately identify ChatGPT text as belonging to any particular category.
The reason why the OpenAI Text Classifier fails to detect ChatGPT text is because of its reliance on traditional NLP techniques such as word embeddings and bag-of-words models. These techniques are not able to capture the nuances of natural language conversations that are generated by ChatGPT. For example, when two people are having a conversation, they often use words and phrases that have multiple meanings or connotations depending on context. This type of subtlety is difficult for traditional NLP techniques to capture accurately.
In addition, ChatGPT uses a deep learning model which allows it to generate responses based on context and previous user input. This means that each response generated by ChatGPT may be slightly different from one another even if they are responding to the same input from the user. This makes it difficult for traditional NLP techniques such as word embeddings and bag-of-words models to accurately identify these responses as belonging to a particular category or sentiment.
The failure of OpenAI’s Text Classifier in detecting ChatGPT text highlights an important limitation of traditional NLP techniques when dealing with more complex forms of natural language processing such as conversational AI systems like ChatGPT. In order for these systems to be successful, they need to be able to accurately identify subtle nuances in conversations which traditional NLP techniques are unable to do effectively. As such, there is a need for new methods and techniques which can better capture these nuances in order for conversational AI systems like ChatGPT to be successful in their tasks.
In conclusion, while OpenAI’s Text Classifier is an effective tool for natural language processing tasks such as sentiment analysis and topic classification, it fails when dealing with more complex forms of natural language processing such as conversational AI systems like ChatGPT due its reliance on traditional NLP techniques which are unable capture subtle nuances in conversations effectively. As such, there is a need for new methods and techniques which can better capture these nuances in order for conversational AI systems like ChatGPT.
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