Decoding LLM Parameters, Part 2: Top-P (Nucleus Sampling)

LLM Parameters

Like any machine learning model, large language models have various parameters that control the variance of the generated text output. We have started a multi-part series to explain the impact of these parameters in detail. We will conclude by striking the perfect balance in content generation using all of these parameters discussed in our multi-part series.

Welcome to the second part, where we discuss another well-known parameter, “Top-P.”

Top-P (Nucleus Sampling)

If the goal is to control the diversity of the model output, then Top-P is the one for you. Lower Top-P forces the model to use the most probable words, whereas higher Top-P forces the model to use more diverse words, increasing creativity.

Let us look at Top-P in action with the following code and output.

Python

 

Output:

PowerShell

 

Now let’s understand the output.

  • Top-P 0.1 – Very Conservative: Since the model selects from the top 10% of the probable following word choices, there is a lot of repetition in the generated content. Hence, this response lacks diversity and is also uninformative most of the time.
  • Top-P 0.3 – Conservative: The model selects from the top 30% of the probable following word choices, so it is slightly less conservative than the previous Top-P Setting. As you can see from the output, this has not improved content generation, and the prompt was repeated throughout the completion. In this case, the prompt’s repetition means the most probable continuation after the prompt for the model seems to be the prompt itself.
  • Top-P 0.5 – Balanced: This is where you see the model listing some numbered strategies for the first time. You still see some repetition in this setting as well. But the bottom line is that at this Top-P setting, the model starts to incorporate a broader range of words. The output is a mix of standard advice with some inconsistencies. This Top-P value allows for improved creativity but still struggles with depth of information.
  • Top-P 0.7 – Creative: In this case, the model can select from a broader range of words, and as you can see, the response is shifting towards a narrative style. The content is more creative as it now involves a scenario where a person is dealing with stress. The downside is the loss of focus, as the emphasis was not on managing stress but on the difficulties in coping with stress. 
  • Top-P 0.9 – Very Creative: In this setting, the model has access to a wide range of vocabulary and ideas including less probable words and concepts. This setting enabled the model to use more expressive language. Again the downside of being very creative is that the model deviates from the prompt in the quest for producing rich and varied content.

The critical thing to note from the above exercise is how the content changes with the change in the Top-P setting. It also gives us an idea that this parameter is not the only one that needs to be handled for variation in content and its relevancy.

Now, let us look at Top-P’s impact on a couple of use cases, just like the previous part of this series on “Creative Story Generation” and “Technical Explanation.”

Python

 

Output:

PowerShell

 

Now let us break down and analyze the output for creative story generation and technical explanation based on the Top-P settings and how the output was impacted.

In order to effectively demonstrate the impact of Top-P we have incorporated better prompts to steer the output in a way that the impact is observed easily.

Creative Story Generation

  • Low Top-P (Negative Impact): As you can see with the lower Top-P, the model is restricted to the use of words or phrases and hence causes repetition and redundancy. The creativity is also limited in this case as the model tries not to introduce new ideas. But if you notice, the logical flow is still maintained, and the model stays on topic, which is typical of lower Top-P values.
  • High Top-P (Perfect Impact): In this case, the model introduces new concepts and adds a creative angle to the narration. Broader vocabulary is used, adding depth and richness to the text. However, due to increased creativity, logical flow has been curbed.

The contrast between the two narratives clearly shows the impact of Top-P, making it easy to understand how it affects creative writing.

Technical Explanation

  • High Top-P (Negative Impact): As you can see, high Top-P negatively impacts technical explanations by preventing a logical flow and deviating from the topic. The model also introduces irrelevant information which is not pertinent to the explanation.
  • Optimal Top-P ( Perfect Impact): The explanation is more coherent and close to the topic with optimal Top-P. The content aligns more with the prompt and balances accuracy and expression well. The reliability of the information is enhanced because the model is limited to more probable words.

Conclusion

With this experiment, we have successfully showcased the importance of the Top-P parameter in controlling the randomness and creativity of the generated text. We first looked at a single prompt and how the output varies with varying Top-P and then took a more use-case-based approach to how Top-P controls the output based on the use case.

However, from the previous and this part of the series, we have noticed that individually, each parameter does not do enough justice to the quality of content generation. That is why it is essential to look at the impact of all of these parameters, and we will be doing that as the final part of this series.

Source:
https://dzone.com/articles/decoding-llm-parameters-top-p