How do I improve token coherence in generative text models that use attention mechanisms

Question

With the help of Python programming, can you tell me How to improve token coherence in generative text models that use attention mechanisms?

Answer 1

To improve token coherence in generative text models with attention mechanisms, you can refer to these approaches below:

• Use Pre-trained Models like GPT-2 for learned language patterns.
• Adjust Attention: Increase attention heads/layers for better context capture.
• Sampling Techniques: Use Top-k or Top-p (nucleus) sampling for more relevant tokens.
• Temperature Scaling: Control randomness in predictions.
• Repetition Penalty: Discourage repeated phrases for better diversity.

Here is the code snippet which you can refer to:

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

• Top-k Sampling: Restricts the sampling to the most likely tokens, maintaining relevance.
• Top-p Sampling: Ensures that only tokens with a cumulative probability above p are considered, improving diversity without sacrificing coherence.
• Temperature Scaling: Controls the level of randomness in token generation.
• Repetition Penalty: Avoids repetitive output, enhancing the coherence of the text.

Hence, these strategies should improve the coherence of generated text by balancing creativity and relevance.