In the context of AI, what does a transformer primarily process?

The transformer model is primarily designed to process sequential data, such as natural language. This architecture allows it to handle input data where the order and context of elements are crucial for understanding meaning. In natural language processing (NLP), for instance, the transformer can effectively capture relationships between words in a sentence, regardless of their distance from each other. This ability is essential for tasks like translation, text generation, and sentiment analysis, where understanding context leads to better outcomes.

Transformers utilize mechanisms like self-attention and positional encoding to analyze and weigh the significance of each word in relation to others in a sequence. This capability explains why they have revolutionized NLP and made significant strides in understanding complex contextual relationships compared to earlier models. Therefore, the capacity of transformers to effectively process and generate sequential data is what sets them apart in the AI domain.