<ul><li>The study explores the role of embedding collapse in Prompt-Tuning for language models.</li><li>Embedding priors are designed and compared with Soft and Deep Prompt-Tuning methods.</li><li>The findings suggest that priors strongly influence the position of tuned embeddings.</li><li>The research raises questions about the significance of a single activation cluster for large language models' generalization abilities.</li></ul>

Exploring Embedding Priors in Prompt-Tuning for Improved Interpretability and Control

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