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How Self-Attention Powers Large Language Models When you interact with ChatGPT or similar AI systems, it often feels like the model understands your entire sentence or paragraph all at once. This is not a coincidence. The underlying reason is a mechanism called self-attention , which sits at the heart of transformer-based models. Self-attention gives large language models their ability to reason across long sequences, disambiguate meaning, and respond coherently. Without it, models would struggle to handle tasks like translation, summarization, question answering, or conversation. What Is Self-Attention Doing Self-attention is a method for learning relationships between words in a sequence by assigning weights based on how important each word is to another. Rather than looking only at nearby words like RNNs or CNNs, self-attention allows each word to consider all the words in the input regardless of their position. For example, in the sentence The key...

Collaborative filtering is commonly used for recommender systems. These techniques aim to fill in the missing entries of a user-item association matrix. spark.ml currently supports model-based collaborative filtering, in which users and products are described by a small set of latent factors that can be used to predict missing entries. spark.ml uses the alternating least squares (ALS) algorithm to learn these latent factors. The implementation in spark.ml has the following parameters: numBlocks is the number of blocks the users and items will be partitioned into in order to parallelize computation (defaults to 10). rank is the number of latent factors in the model (defaults to 10). maxIter is the maximum number of iterations to run (defaults to 10). regParam specifies the regularization parameter in ALS (defaults to 1.0). implicitPrefs specifies whether to use the explicit feedback ALS variant or one adapted for implicit feedback data (defaults to false...