How can you implement dynamic sampling techniques like top-k sampling and top-p sampling for text generation with GPT-3

Question

Can you tell me how to implement dynamic sampling techniques like top-k and top-p sampling for text generation with GPT-3?

Answer 1

To implement dynamic sampling techniques like top-k sampling and top-p (nucleus) sampling for text generation with GPT-3, you adjust the probability distribution of the next token selection. Both techniques help improve diversity and control randomness.

Here is the code you can refer to:

In the above code, we are using the following:

• Top-K Sampling:

  • Limits the selection to the kkk-most probable tokens.
  • Use it when you want to control randomness.

• Top-P Sampling:

  • Selects tokens cumulatively until the probability reaches ppp.
  • Provides adaptive flexibility based on the distribution.

• Diversity Control:

  • Both methods improve over greedy sampling, producing more coherent and creative outputs.

Hence, by referring to this, you can  implement dynamic sampling techniques like top-k sampling and top-p sampling for text generation with GPT-3