Debugging an Autoregressive Model's Attention
An AI developer is debugging a small autoregressive language model designed for text generation. The model's purpose is to predict the next word in a sequence based only on the words that came before it. For the input sequence 'The cat sat on', the developer extracts the 4x4 attention weight matrix shown below, where each row corresponds to a token attending to other tokens (e.g., row 1 is for 'cat' attending to 'The', 'cat', 'sat', 'on').
Analyze this matrix. What is the fundamental problem with this attention mechanism for its intended purpose, and what specific value(s) in the matrix demonstrate this problem?
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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
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
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Empirical Science
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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