How can I avoid sampling bias in my generative model during inference

Question

With the help of code, can you explain how I can avoid sampling bias in my generative model during inference?

Answer 1

In order to avoid sampling bias in my generative model during inference, you can refer to the following steps below:

• Temperature Scaling: Adjust the temperature to control randomness in predictions.
• Top-k Sampling: Limit sampling to the top-k most probable tokens to ensure diversity.
• Top-up (Nucleus) Sampling: Sample from the smallest set of tokens with cumulative probability ≥ p.
• Repetition Penalty: Penalize repeated tokens to avoid biased sequences.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Temperature Scaling: Controls randomness by adjusting the temperature, balancing between diversity and coherence.
• Top-k Sampling: Limits sampling to the top-k most probable tokens for diversity.
• Top-up (Nucleus) Sampling: Samples from the smallest set of tokens with cumulative probability ≥ p for better diversity.
• Repetition Penalty: Discourages repeated tokens to avoid biased or repetitive outputs.

Hence, by referring to above, you can avoid sampling bias in my generative model during inference.