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3 questions for ... App-Developer Zaid

Zaid, you have developed apps for DaSense that integrate ChatGPT. How does GPT generate text and what techniques are used to ensure the generated text is coherent and fluid?

GPT (Generative Pre-trained Transformer) is an artificial intelligence-based algorithm that can automatically generate text. GPT is based on the Transformer model, a type of neural network specifically designed to process sequences of symbols such as words or characters. GPT uses an approach called "unsupervised learning" in which the model learns from a large amount of textual data without specific instruction or supervision. It is trained on a huge amount of text data to develop an understanding of the language and grammar. By applying attentional mechanisms and stitching together multiple layers of the neural network, GPT is able to generate text that can respond to specific prompts or context.

Several techniques are used to ensure that the generated text is coherent and fluid, including: Context Awareness: GPT is able to understand and consider the context of the prompt to generate text that is coherent with the context.

Repetition Avoidance: GPT uses a technique called "nucleus sampling" to reduce the likelihood of repeated text and ensure that the generated text is fluid and varied.

Forced Topics: In some cases, GPT can also be forced to focus on a specific topic or context to ensure greater coherence and relevance of the generated text.

Text length regulation: GPT can be set to generate a certain number of words or characters to ensure that the generated text is not too long or too short and can keep the reader's attention.

Human Expert Assessment: The generated texts can be assessed by human experts to ensure they are coherent, fluent, and fit the context and prompt requirements. These techniques help ensure that the text generated by GPT is coherent, fluid, and relevant.

How can we make sure ChatGPT doesn't hallucinate i.e. give misleading or wrong answers? What are hallucinations in this context?

As an AI model, ChatGPT has no intuition or deep understanding of meanings, concepts or the world around us. It is based solely on the patterns it has learned from a large amount of data. However, sometimes the model can "hallucinate", ie generate an answer that is not based on the data or is incorrect or misleading.

To ensure that ChatGPT does not generate hallucinations, there are several techniques that can be used. One possibility is to feed the model with sufficient and high-quality training data. This means that the model should be trained on a diverse data set covering different topics, perspectives, and contexts. It is also important to regularly test the model on new data to ensure that it works correctly and does not create hallucinations.

Another option is to give the model special instructions to ensure it doesn't generate incorrect or misleading answers. For example, we can teach the model certain rules about how to answer certain types of questions, or teach it to ignore certain types of information that might be irrelevant or misleading.

In this context, hallucination refers to responses that are not based on the request or are misleading or incorrect. An example of a hallucination could be when ChatGPT asked "What is the capital of France?" with the answer "The capital of France is New York" when the correct answer is "Paris".

How can we ensure that ChatGPT's responses are correct? What methods do you use to verify ChatGPT responses?

As an AI model, ChatGPT cannot offer absolute certainty about the correctness of the answers due to its underlying approach. Still, there are several methods that can be used to verify and score ChatGPT's responses. Data Quality and Training: The quality and diversity of the training data can affect the quality of ChatGPT's responses. Therefore, the model should be trained on a large and high-quality data set covering different topics, contexts and perspectives. Careful selection and verification of data can help improve the quality of responses. Human Expert Verification: One method to verify ChatGPT's responses is to have them verified by human experts. This can be done by conducting validation or testing processes where human experts evaluate the answers and compare them to the actual answers or facts.

Fact Checking: Another approach is to verify ChatGPT's responses using fact checking tools. These tools can automatically check whether or not the answer agrees with known facts or truths. If the answer does not match the facts, the model can be adjusted accordingly.

Feedback Mechanisms: Feedback mechanisms allow users to provide feedback on ChatGPT's responses in order to improve the model. These can be integrated in the form of ratings or comments on the website or application using ChatGPT.

Monitoring the Model: Monitoring ChatGPT is an important step to regularly review and evaluate the model's responses. It is important to continually monitor the model for performance, accuracy, and consistency to identify problems or errors and adjust the model accordingly.


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