Lesson 5: ChatGPT Parameters - A Deeper Understanding
PreviousLesson 4: Diving into ChatGPT Platforms: Playground and BotHubNextLesson 6: Expanding Capabilities: Plugins for ChatGPT and Working Through API
Last updated
Last updated
Welcome to the new lesson dedicated to the parameters of ChatGPT-4! To fully unlock the potential of this unique chatbot, it's important to understand its settings. In this lesson, we will delve into such parameters as tokens, temperature, top_p, Frequency penalty, Presence penalty, and context length.
Description of ChatGPT-4 Parameters
Tokens: In the world of AI, tokens are considered the smallest units of text that a model can process. In the case of ChatGPT-4, one token usually corresponds to one character but may also correspond to an entire word, depending on the language.
For example, in Russian, 1 token is approximately equal to 2 non-space characters, while in English, it's about 4 non-space characters.
The number of tokens in your requests and responses affects how long the model will take to generate a response, and how much you will have to pay for each request.
Temperature: The "Temperature" parameter controls the randomness of the model's responses. A higher temperature value (closer to 1) makes responses more random, while a lower value (closer to 0) makes responses more predictable and conservative.
Top_p (Nucleus Sampling): Top_p is the probability with which the model selects the next token when generating text. A value of 1 means the model will consider all possible tokens for the next word, whereas a value close to 0 will make the model choose only the most likely tokens.
Frequency penalty: This parameter helps control the frequency of certain words in the model's responses. A higher penalty value will decrease the likelihood of word repetitions, while a lower value will allow the model to use the same words more frequently.
Presence penalty: This parameter controls how much the model prefers to use words and phrases that are already present in the context. A higher value will increase the chances of using new words and phrases, while a lower value will allow the model to repeat words and phrases already mentioned.
Context length: This parameter determines how long the text can be that the model uses to generate a response. A longer context allows the model to "remember" more information from previous messages, but can also lead to longer processing times and costs.
Applying Parameters in Practice
Now let's see how these parameters can change the output of the model. Suppose we want ChatGPT-4 to write a story about a knight.
Temperature = 0.2
"Write a short story about a team of superheroes"
Response:
Temperature = 1
The same request: "Write a story about a team of superheroes"
Response:
Do you see the difference? At a low temperature, the story was predictable and classic, while at a high temperature, we got an unusual and original story, however, there were strange words in the output.
And now let's experiment!
Practical Assignment
Task:
Experiment with different parameters in ChatGPT-4 to understand how each one affects the model's results.
Instruction:
Create several requests to ChatGPT-4, changing the values of temperature and top_p. Note how the model's responses change with different settings.
Conduct a series of experiments with the parameters Frequency penalty and Presence penalty. Pay attention to the influence of these parameters on the diversity and originality of the responses.
Try creating a request with a large number of tokens and a request with a small number of tokens. Note the processing time of the request and the quality of the response.
Example of execution:
Request: "Tell me a story about a pirate". Let's try temperature 0.7 and top_p 0.8:
Then we change to temperature 0.3 and top_p 0.5:
Compare the stories obtained.
The same request, but now with a Frequency penalty of 0.5 and a Presence penalty of 0.5, then we change the values to a Frequency penalty of -0.5 and a Presence penalty of -0.5. Compare the stories obtained.
First, we make a request "Tell me a story about a pirate" with a context length of 10 tokens, then we change the context length to 100 tokens. We compare the processing time and the quality of the stories.
Don't forget to record your observations and conclusions. This will help you develop skills working with different parameters and improve your understanding of how the model works.
Conclusion
Model parameters are an important tool for fine-tuning interactions with ChatGPT-4. Understanding how they work and their impact on results will allow you to use the chatbot as efficiently as possible, making its responses more predictable, interesting, or original depending on your needs. Try different combinations and find your ideal parameters for working with ChatGPT-4! Good luck with your training!
