In Conversation With AI
I Wondered How ChatGPT Could Challenge Incorrect Premises — So I Asked ChatGPT Herself
When an AI explains itself
ChatGPT, developed by OpenAI, is a chatbot built on top of GPT-3 — a family of large language models, where GPT stands for Generative Pre-trained Transformer.
ChatGPT is trained to answer questions, provide information, and converse on a wide range of topics. As OpenAI puts it, it is possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
The below articles demonstrate ChatGPT’s capabilities to often, if not always, challenge incorrect premises.
3 Questions That Tricked ChatGPT Into Replying Awfully Wrong
5 Questions That Could Not Trick ChatGPT
Having wondered how it could identify and challenge the incorrect premises, I asked ChatGPT herself.
Here is how ChatGPT explains itself.
Sekhar: Hello!
ChatGPT: Hello! How can I help you today?
Sekhar: Do you know ChatGPT?
ChatGPT: Yes, I am ChatGPT. It’s a large language model developed by OpenAI. Is there something specific you would like to know or accomplish with my help?
Sekhar: In the OpenAI’s website, they mentioned it is possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. So I was wondering how do you (the ChatGPT) gain insight into which are the incorrect premises and which are the correct ones?
ChatGPT: ChatGPT is a machine learning model that has been trained on a large dataset of text. It uses this training data to make predictions about what words or phrases are likely to come next in a given context.
When it comes to recognizing incorrect premises or rejecting inappropriate requests, the model relies on patterns and statistics it has learned from the training data. However, it doesn’t have the ability to truly understand the meaning of the text or context, so it can’t truly “know” if a statement is correct or not.
It’s important to note that while ChatGPT is a very advanced AI model, it is not capable of understanding the meaning or context of the text in the same way that a human does. Therefore, it’s possible that the model may generate an incorrect response, or fail to recognize that a statement is incorrect.
Sekhar: Can you elaborate ‘relying on patterns and statistics learned’?
ChatGPT: Yes, certainly. When the model is trained on a dataset of text, it learns patterns and statistics about the way words and phrases are used in that language. For example, it might learn that certain words often appear together in a certain order, or that certain words are more likely to appear in certain types of text.
When the model is then given a new piece of text, it uses these learned patterns and statistics to generate a response. For example, if the model has learned that the word “dog” is often followed by the word “bark,” it may be more likely to generate the word “bark” as the next word in a sentence that starts with “dog.”
In the context of recognizing incorrect premises or rejecting inappropriate requests, the model uses patterns and statistics it has learned from the training data to generate a response. However, since the model doesn’t truly understand the meaning of the text, it’s possible that it may generate a response that is not appropriate or that is based on an incorrect premise.
So, in summary, the model relies on patterns and statistics it has learned from the training data to generate responses, but it doesn’t have the ability to truly understand the meaning of the text or context.
Sekhar: You still have not explained me how reocognizing incorrect responses is achieved through learned patterns and statistics!
ChatGPT: I apologize for any confusion. Recognizing incorrect premises or rejecting inappropriate requests is a challenging task for a language model like ChatGPT because it doesn’t have the ability to truly understand the meaning of the text or context. Therefore, it can’t truly “know” if a statement is correct or not.
What the model can do is use patterns and statistics it has learned from the training data to make an educated guess about whether a statement is likely to be correct or not. For example, if the model has been trained on a dataset of scientific text, it may be more likely to recognize that a statement about the earth being flat is incorrect because it has seen many examples of scientific text that state the earth is round.
Similarly, the model can use patterns and statistics it has learned from the training data to recognize and reject inappropriate requests, such as hate speech. For example, if the model has been trained on a dataset of text where hate speech is not present, it may be able to recognize a request that contains hate speech as inappropriate and reject it.
It’s important to note that the model can make educated guesses based on the patterns and statistics it has learned, but it can’t truly understand the meaning or context of the text in the same way that a human does. Therefore, it’s possible that the model may generate an incorrect response, or fail to recognize that a statement is incorrect.
Sekhar: Okay. I got some sense of how it is done now. Thanks for explaining yourself.
ChatGPT: You’re welcome. I’m glad I could help. If you have any more questions or need further clarification, feel free to ask.
Sekhar: Sure. Bye for now.
ChatGPT: Goodbye! Have a great day.
For other interesting conversations with Artificial Intelligence, find the collection In Conversation With AI.
