What does "Attention Bias" mean?

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Attention bias is a technique used in large models that handle both text and images. It helps these models focus better on important information when making decisions.

In many tasks, some parts of the data get too much attention, while other important parts are ignored. This can lead to mistakes and confusing results. Attention bias aims to fix this by making sure all parts of the data are considered fairly.

When the model is looking at an image or reading text, it checks which parts are being focused on too much. Then, it adjusts how much weight those parts have in the decision-making process. This way, it can better understand details and relationships, leading to more accurate outcomes.

Overall, attention bias improves the ability of models to answer questions and respond correctly by creating a more balanced view of the information they are working with.