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In contrast to methods like prefix fine-tuning that require modifications to the internal architecture of a Large Language Model, an alternative approach is to separate the soft prompts from the LLM. This separation preserves the original model architecture, which significantly improves deployment efficiency across different tasks since the core model remains unchanged. Prompt tuning is a key example of this method, as it confines its modifications to the input layer, leaving the rest of the model intact.

Architectural Preservation by Separating Soft Prompts from LLMs

A development team is tasked with adapting a single, massive pre-trained language model for ten different specialized functions (e.g., legal document analysis, medical chatbot, creative writing assistant). They choose an adaptation method that introduces a small set of new, learnable parameters for each task while keeping the original model's millions of parameters completely frozen. Explain the primary advantage of this approach regarding storage and deployment efficiency compared to creating a fine-tuned copy of the entire model for each task.

Efficiency of Parameter-Efficient Tuning

Match each description of a model adaptation method with its corresponding impact on the model's architecture and deployment efficiency.

A technology company plans to deploy a single, large, pre-trained language model to serve three distinct functions: a customer support chatbot, a document summarizer, and a code generator. To optimize for efficiency, they want to avoid storing and maintaining multiple, separate copies of the large model. Which approach best achieves this goal by keeping the original model's architecture intact?

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