What techniques resolve length truncation issues when generating long-form text using transformers

Question

Can you tell me What techniques resolve length truncation issues when generating long-form text using transformers?

Answer 1

Length truncation in long-form text generation can be resolved by using techniques like sliding window attention, chunked input processing, extending the model’s max length, and leveraging models optimized for long sequences (like Longformer or GPT-3).

Here is the code snippet you can refer to:

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

• Extends the max_length parameter to handle longer text outputs.
• Uses nucleus sampling (top_p) and temperature control for quality and coherence.
• Applies a repetition penalty to prevent redundant or circular content.

Hence, by adjusting model parameters and managing sampling techniques, we effectively address length truncation and generate coherent, detailed long-form text.