What is Top-p (Nucleus Sampling)?

Top-p is a parameter that shapes the diversity and quality of the text produced by a generative AI model. When a model generates text, it evaluates various possible next tokens (words or subwords) based on probability. Top-p narrows down the model's choices by calculating the cumulative probability of the available tokens and selecting those that add up to a certain threshold, which is defined by p. This threshold is a value between 0 and 1, and it helps determine how many tokens to consider for generating the next word.

This technique is often referred to as nucleus sampling because the model works within a "nucleus" of highly probable tokens, dynamically adjusting the scope of consideration.

How Does Top-p Work?

Let’s break down how top-p operates using an example.

1. Setting Top-p to 1 (100% of tokens): When top-p is set to 1, the model evaluates all possible tokens available. The cumulative probability will include every possible token, from the most likely to the least probable. This means the model has the freedom to select even rare or creative options like "majestic" or "whimsical" from the full list of available tokens.

   The result is greater diversity and more varied text, as the model can choose from a broader pool of words, even those with lower likelihoods.

2. Setting Top-p to 0.6 (60% of tokens): When top-p is set to 0.6, the model limits its choices to only the top 60% of tokens based on cumulative probability. It excludes tokens with lower likelihoods, resulting in a more focused and predictable output.

   By narrowing the set of possible tokens, the model’s output becomes less diverse but more likely to produce coherent and conventional results. This setting is useful when you want the model to stay within a certain set of more probable and relevant words.

Key Considerations

• Diversity Control: Adjusting top-p gives you control over the level of diversity in the generated text. A higher top-p (e.g., 1.0) gives the model more freedom, leading to more diverse outputs. A lower top-p (e.g., 0.6) restricts the output, making it more predictable.

• Temperature vs. Top-p: While both temperature and top-p control randomness, they do so in different ways. Temperature adjusts the shape of the probability distribution, making some tokens more likely to be selected over others. In contrast, top-p directly limits the set of considered tokens, restricting the model to those whose cumulative probability surpasses the threshold.

• Use Case: Top-p is often reserved for advanced use cases, where fine-tuning of the diversity or predictability of outputs is required. For most tasks, adjusting temperature is sufficient.

Example Scenario

Let’s say you’re working on a creative project and want to generate a poetic line. If you set top-p to 1.0, the model will consider all possible tokens, and the output might include unexpected or poetic words, giving you creative and varied results.

In contrast, if you set top-p to 0.6, the output will be more constrained and consistent, with the model choosing from a narrower set of highly probable tokens. This might be useful for generating text that needs to be more formal or structured.

Activity 1: Experimenting with Top-p Settings

Objective: Understand the impact of different top-p values on the output.

Instructions:

1. Set up a generative AI model to generate text from the prompt: "The city skyline at dusk is."

2. Generate the output with top-p set to 1.0.

3. Generate the output with top-p set to 0.6.

4. Compare the outputs and analyze the differences in diversity and creativity.

Reflection Questions:

• How does the level of creativity change when the model uses a higher top-p value (1.0)?

• How does the more limited token pool (with top-p set to 0.6) affect the output's style and coherence?

Activity 2: Comparing Temperature and Top-p Effects

Objective: Compare how temperature and top-p influence the model’s output.

Instructions:

1. Use a generative model to create text from the prompt: "The future of technology will ____"

2. First, experiment with temperature: set it to different values like 0.5, 1.0, and 1.5, and observe the changes in the output.

3. Next, adjust the top-p parameter (e.g., set it to 0.6 or 1) and generate responses.

4. Compare the results from the temperature adjustments and the top-p adjustments.

Reflection Questions:

• What differences do you notice when changing temperature versus top-p?

• Which setting results in more predictable, structured output, and which one produces more creative or varied responses?