Turning Up the Heat: Understanding LLM Temperature

Think of LLM temperature like a dial on a radio. At a low temperature, the LLM acts more like a conservative DJ, sticking to the most popular hits (the most likely word choices based on its training data). This results in factual and safe outputs, ideal for tasks like question answering.

On the other hand, crank up the temperature, and the LLM transforms into a wild DJ, venturing beyond the familiar tunes. This injects randomness, leading to more creative and diverse outputs. It's great for brainstorming or generating different writing styles but be prepared for some occasional off-key notes (gibberish or nonsensical responses).

The magic lies in finding the right temperature for the job. Here's a quick guide:

• High Temperature (T > 1): More creative, unusual outputs, but prone to errors. Use it for brainstorming or creative writing prompts.
• Low Temperature (0 < T < 1): Factual, predictable outputs, ideal for tasks requiring accuracy, like question answering or summarizing factual topics.
• Default Temperature (T > 0.7 and T < 1): The middle ground, balancing randomness and determinism. This is a good starting point for many tasks.

Remember, temperature is just one tool in the LLM toolbox. By experimenting with temperature and other settings like prompts, you can fine-tune the LLM to deliver the kind of outputs you need.

Hands-On Temperature Tuning:

import requests
import json

url = "https://api.openai.com/v1/chat/completions"

payload = json.dumps({
  "model": "gpt-3.5-turbo",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful programming assistant."
    },
    {
      "role": "user",
      "content": "Write a sarcastic one-liner resignation letter."
    }
  ],
  "temperature": 1.6
})
headers = {
  'Content-Type': 'application/json',
  'Authorization': 'Bearer sk-i-wont-tell',
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)

#output
Dear boss, I'm resigning to pursue my dream of not working here. Cheers.