In the context of large language models (LLMs), temperature and top-K are parameters used to control the randomness and diversity of the generated text. They determine how the model selects words (tokens) when generating responses. Here’s what each one does:
1. Temperature
- What it does:
- The temperature parameter adjusts the “creativity” of the model’s outputs. It controls how deterministic or random the token sampling process is.
- How it works:
- A low temperature (e.g., 0.2) makes the model more deterministic and focused, favoring tokens with the highest probabilities. This is useful for tasks requiring precision or when predictable outputs are preferred.
- A high temperature (e.g., 1.0 or more) increases randomness, allowing the model to explore less likely tokens. This is ideal for generating creative or diverse outputs.
- At temperature = 0, the model selects the highest-probability token every time, making it fully deterministic.
- Example:
- Low temperature (0.2):
Input: “Once upon a time, there was a…”
Output: “princess who lived in a castle.” - High temperature (1.0):
Input: “Once upon a time, there was a…”
Output: “dragon guarding a mysterious treasure.”
- Low temperature (0.2):
2. Top-K Sampling
- What it does:
- Top-K limits the model’s token selection to the top K highest-probability tokens. This reduces randomness by restricting choices to a smaller, high-probability set.
- How it works:
- K = 1: Only the highest-probability token is selected (fully deterministic).
- Higher K values (e.g., 40, 50): Allow more diverse tokens to be considered, introducing some randomness but still narrowing the choice compared to the full distribution.
- Why use it:
- It ensures that only the most relevant or reasonable options are considered, avoiding extremely rare or nonsensical tokens while still allowing variety in outputs.
- Example:
- Input: “The sky is…”
- Top-K (K=1): “blue.”
- Top-K (K=50): “blue,” “cloudy,” “bright,” “gray,” etc.
When combined:
- Temperature and top-K can work together to balance creativity and coherence:
- High temperature + High top-K: Generates diverse and creative text.
- Low temperature + Low top-K: Generates focused and precise text.
- Medium temperature + Medium top-K: Balances creativity and reliability.
This combination allows fine-tuning of how the model behaves, depending on the task (e.g., storytelling vs. answering factual questions).