Learn Before
Role of Causal Attention in Autoregressive Language Models
Causal attention is fundamental to autoregressive language models, which are designed to predict the next token in a sequence based solely on the preceding tokens (the 'left-context'). The causal attention mechanism enforces this constraint by masking out future positions, ensuring that the model's output at any given position i is only influenced by tokens from positions 0 to i.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Role of Causal Attention in Autoregressive Language Models
Causal Attention Output for a Single Token
Visualization of Query-Key Dot Products in Causal Attention
An autoregressive model calculates a square attention weight matrix using the formula:
Softmax((QK^T / sqrt(d)) + Mask). The purpose of theMaskcomponent is to prevent any token from attending to subsequent tokens in the sequence. Which statement best describes the resulting attention weight matrix?An autoregressive model is processing a sequence of 4 tokens. To ensure that the prediction for any given token is based only on the tokens that came before it and the token itself, a specific structure is imposed on the attention weight matrix. Which of the following 4x4 matrices correctly illustrates this structure, where 'α' represents a calculated, non-zero attention weight and '0' represents a weight that has been forcibly set to zero?
Applying a Causal Mask to Attention Scores
Learn After
An engineer is training an autoregressive language model designed to generate text one word at a time. Due to a configuration error, the attention mechanism is allowed to see all tokens in the input sequence, including those that appear later in the sequence, rather than only the preceding ones. The model trains successfully to a very low loss on its training data. What is the most likely outcome when this trained model is later used to generate new text, starting from a prompt?
Debugging an Autoregressive Model's Attention
Enforcing Autoregressive Behavior