What are the trade-offs between autoregressive and bidirectional transformers in Generative AI

Question

With the help of python programming can you tell me What are the trade-offs between autoregressive and bidirectional transformers in Generative AI?

Answer 1

Autoregressive transformers generate text sequentially, making them slower but more suitable for tasks requiring fine-grained control, while bidirectional transformers process the entire sequence at once, enhancing context understanding but generally being less suited for sequence generation.

Here is the code snippet showing how It is done:

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

• Autoregressive (GPT-2): Generates text one token at a time, making it suitable for tasks like text generation.
• Bidirectional (BERT): Processes entire sequences to understand context but doesn't directly generate text, focusing on tasks like masked language modeling.
• Task Suitability: Autoregressive models excel in a generation, while bidirectional models excel at understanding context.

Hence, autoregressive transformers offer sequential generation and flexibility, while bidirectional models enhance contextual understanding but are limited in generating content. Choose based on the task's requirements.