Global Tokens in Attention
Global tokens are a widely-used technique in attention mechanisms for combining local and long-term context. This approach designates a few tokens at the beginning of a sequence as 'global,' making them accessible to all other tokens during attention calculations. Often implemented alongside sparse attention models, this method serves as a form of global memory. It offers the advantage of stabilizing model performance by smoothing the Softmax distribution of attention weights, but it also introduces a trade-off: the fixed size of this global memory can lead to information loss, creating a tension between representational capacity and computational cost.
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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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Global Tokens in Attention
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An engineer is designing a language model to process and answer questions about very long documents, such as legal contracts or novels. The model needs to understand the immediate context of a specific clause or sentence while also retaining key information and themes from the entire document. Which architectural approach is most suitable for this task?
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A language model is designed with an efficient attention mechanism where each token can only interact with the 16 tokens immediately preceding and following it. This model performs poorly on tasks that require summarizing a long document, as it fails to connect information from the introduction to the conclusion. Which of the following architectural changes is most specifically designed to solve this type of long-range dependency issue while largely preserving the model's computational efficiency?
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In an attention mechanism that uses a fixed number of designated tokens as a form of global memory, continuously increasing the number of these special tokens is a guaranteed strategy to improve model performance on all tasks without introducing any negative consequences.