How do I fix repetitive text generation in models like GPT-2 or T5

Question

With the help of Python code, can you explain How to fix repetitive text generation in models like GPT-2 or T5?

Answer 1

To fix repetitive text generation in models like GPT-2 or T5, you can follow the following steps:

• Top-k Sampling: Limit the number of possible next tokens to the top k most probable ones.
• Top-p (Nucleus) Sampling: Use cumulative probability to sample from the smallest possible set of tokens that make up a given probability.
• Temperature Scaling: Adjust the sampling temperature to control the randomness of predictions (higher temp = more randomness).
• Repetition Penalty: Penalize previously generated tokens to reduce repetition.
• Beam Search with Diversity: Use beam search with a diversity penalty to avoid generating repeated sequences.

Here is the code snippet you can refer to:

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

• Repetition Penalty: Reduces the likelihood of generating previously used tokens.
• Top-k Sampling: Limits the number of tokens to sample from to reduce repetition.
• Temperature Scaling: Adjusts the randomness of predictions to avoid deterministic outputs.
• Top-p Sampling: Selects tokens based on cumulative probability to allow more diversity.

Hence, these strategies help generate more varied and less repetitive text in models like GPT-2 or T5.