What is the purpose of temperature in prompting.

The purpose of Temperature in prompting is to control the degree of randomness in token selection when a Large Language Model generates a response.

Here's a breakdown of its role:

• Controlling Randomness: Temperature is a configuration setting for Large Language Models that affects the unpredictability of the output.
• Low Temperature (closer to 0): Leads to more deterministic and predictable responses. At a temperature of 0, known as greedy decoding, the model always selects the token with the highest probability. This is suitable for tasks where factual precision or a single correct answer is expected, such as mathematical problems or classification tasks where creativity is not needed.
• High Temperature (further from 0, often up to 1 or higher): Encourages more diverse, creative, or unexpected results. As the temperature increases, a wider range of tokens becomes more acceptable options for the next predicted token, similar to how a high softmax temperature accommodates more possibilities. This setting is useful when experimenting with creative outputs or brainstorming.
• Interaction with other settings: Temperature interacts with other sampling settings like Top-K and Top-P. At extreme settings (e.g., Temperature 0), it can make other settings irrelevant.
• Specific Technique Use: In techniques like Self-Consistency, a high temperature setting is crucial to prompt the model multiple times and encourage it to generate diverse reasoning paths to the same problem. For Chain of Thought (CoT) prompting, when seeking a single correct answer based on reasoning, the temperature should generally be set to 0.
• Potential Issues: While higher temperatures increase creativity, they can also lead to text that is less relevant. Inappropriate temperature settings (both low and high) can exacerbate issues like the "repetition loop bug," where the model gets stuck repeating words or phrases.
• Prompt Engineering Best Practice: Experimenting with different model configurations, including temperature, is an important part of the iterative process of prompt engineering to achieve the desired outcome.

Ultimately, adjusting the temperature setting allows the user to control the balance between predictability and creativity in the LLM's output, tailoring it to the specific requirements of the task.