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Standing on the Shoulders of Giant Frozen Language Models
To demonstrate this, we introduce three novel methods for leveraging frozen models: input-dependent prompt tuning, frozen readers, and recursive LMs, each of which vastly improves on current frozen-model approaches.
Technical Report: Auxiliary Tuning and its Application to Conditional Text Generation
We introduce a simple and efficient method, called Auxiliary Tuning, for adapting a pre-trained Language Model to a novel task; we demonstrate this approach on the task of conditional text generation.
Deep density networks and uncertainty in recommender systems
Building robust online content recommendation systems requires learning complex interactions between user preferences and content features.
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