What are the challenges in embedding large contexts in Generative AI for summarization tasks

Question

Can you tell me What the challenges are in embedding large contexts in Generative AI for summarization tasks?

Answer 1

Handling large contexts in summarization tasks is challenging due to token limit constraints, memory inefficiency, and loss of key information in attention mechanisms.

Here is the code snippet you can refer to:

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

• Optimized for Long Contexts: Uses Longformer, which supports up to 4096 tokens with efficient attention.
• Sliding Window Attention: Reduces memory overhead by focusing on local context windows.
• Preserves Important Information: Unlike traditional transformers that truncate text, it keeps relevant details.
• Efficient Memory Usage: Helps avoid GPU memory overflow while handling long documents.
• Improves Summarization Accuracy: Ensures key ideas are retained in large-context AI models.

Hence, by referring to the above, you can handle the challenges.