Attention masking is a technique used in transformer models to control which parts of the input sequence the model can focus on during processing. By applying masks, certain tokens or positions are effectively hidden from the model, guiding its attention mechanism to consider only the relevant parts of the input.
Types of Attention Masks:
- Padding Masks: In sequences of varying lengths, padding tokens are added to standardize input sizes. Padding masks prevent the model from attending to these padding tokens, ensuring they don’t influence the model’s understanding of the actual data.
- Causal (or Look-Ahead) Masks: In tasks like language modeling, it’s crucial that the model doesn’t access future tokens when predicting the next token. Causal masks block attention to future positions, ensuring the model’s predictions are based solely on past and current tokens.
- Content-Based Masks: These masks are applied based on the content of the input, allowing the model to focus on specific parts of the sequence that are deemed important for the task at hand.
Applications of Attention Masking:
- Natural Language Processing (NLP): In tasks like machine translation and text generation, attention masking ensures that the model doesn’t “cheat” by accessing information it shouldn’t have, leading to more accurate and realistic outputs.
- Speech Recognition: In speech-to-text models, attention masking helps the model focus on relevant audio features, improving transcription accuracy.
- Computer Vision: In image processing, attention masking can guide the model to focus on specific regions of an image, enhancing tasks like object detection and segmentation