Generate a multiple choice quiz from the text below. Quiz should contain at least 5 questions. Each answer choice should be on a separate line, with a blank line separating each question.
at the end provide answers too
The GPT-35-Turbo and GPT-4 models are language models that are optimized for conversational interfaces. The models behave differently than the older GPT-3 models. Previous models were text-in and text-out, meaning they accepted a prompt string and returned a completion to append to the prompt. However, the GPT-35-Turbo and GPT-4 models are conversation-in and message-out. The models expect input formatted in a specific chat-like transcript format, and return a completion that represents a model-written message in the chat. While this format was designed specifically for multi-turn conversations, you'll find it can also work well for non-chat scenarios too.
Example:
Q1. What is a GPT-35-Turbo?
A. A lnguage models
B. optimized for conversational interfaces
C. both
D. None of the above
Q2. How is GPT-35-Turbo different from older models?
A. They are text-in and text-out
B. They are conversation-in and message-out
C. Both
D. None of the above
Q3. What is the chat-like transcript format?
A. Input formatted
B. Output formatted
C. Both of the above
D. None of the above
Q4. Can GPT-35-Turbo and GPT-4 models be used in non-chat scenarios?
A. Yes
B. No
C. Rarely used
D. None of the above
Q5. What does GPT-35-Turbo return?
A. A completion to append to the prompt
B. A model-written message in the chat
C. A and B both
D. None of the above
Answers:
1. C
2. B
3. A
4. A
5. B
Case Study -2
Generate a multiple choice quiz from the text below. Quiz should contain at least 5 questions. Each answer choice should be on a separate line, with a blank line separating each question.
at the end provide answers too
Models
The service provides users access to several different models. Each model provides a different capability and price point.
GPT-4 models are the latest available models. These models are currently in preview. For access, existing Azure OpenAI Service customers can apply by filling out this form.
The GPT-3 base models are known as Davinci, Curie, Babbage, and Ada in decreasing order of capability and increasing order of speed.
The Codex series of models is a descendant of GPT-3 and has been trained on both natural language and code to power natural language to code use cases. Learn more about each model on our models concept page.
The following table describes model families currently available in Azure OpenAI Service. Not all models are available in all regions currently. Please refer to the capability table at the bottom for a full breakdown.
Model family Description
GPT-4 A set of models that improve on GPT-3.5 and can understand as well as generate natural language and code. These models are currently in preview.
GPT-3 A series of models that can understand and generate natural language. This includes the new ChatGPT model (preview).
Codex A series of models that can understand and generate code, including translating natural language to code.
Embeddings A set of models that can understand and use embeddings. An embedding is a special format of data representation that can be easily utilized by machine learning models and algorithms. The embedding is an information dense representation of the semantic meaning of a piece of text. Currently, we offer three families of Embeddings models for different functionalities: similarity, text search, and code search.
Q1. What capability can GPT-4 understand?
A. code
B. natural language
C. both
D. None of the above
Q2. What is the latest available GPT-4 model?
A. currently in preview.
B. known as Davinci, Curie, Babbage, and Ada
C. created for translating natural language to code
D. All of the above
Q3. What is the Codex series of models?
A. A series of models that can understand and generate natural language.
B. A descendant of GPT-3 trained on natural language and code
C. A set of models that can understand and use embeddings
D. None of the above
Q4. What do Embeddings models allow for?
A. Representing data in a way that is easily utilized by machine learning models and algorithms.
B. Generate natural language and code
C. Understand and generate code
D. Currently, we offer three families of Embeddings models for different functionalities: similarity, text search, and code search.
Q5. Are all models available in all regions?
A. Yes
B. No
Answers:
Q1. C
Q2. D
Q3. B
Q4. A
Q5. B
