During tokenized sequence generation the attention weights are overly focused on recent tokens How can this bias be reduced

Question

With the help of proper code can you tell me During tokenized sequence generation, the attention weights are overly focused on recent tokens. How can this bias be reduced?

Answer 1

To reduce the bias of attention weights overly focusing on recent tokens, use relative positional embeddings, apply decay masks, or integrate a memory-augmented transformer mechanism.

Here is the code snippet you can refer to:

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

• Relative Positional Embeddings: Configures BERT to use relative positional encoding to balance attention across tokens.
• BERT-Based Model: Uses a transformer model (BertModel) with modified position embedding settings.
• Tokenization: Processes input text with BertTokenizer to prepare it for model inference.
• Forward Pass: Generates hidden states with attention mechanisms adjusted via relative positions.
• Output Analysis: Ensures attention distribution is more evenly spread across the sequence.

Hence, by implementing relative positional embeddings in the transformer, we mitigate the excessive focus on recent tokens, leading to a more balanced and contextually aware sequence generation.