Soft knowledge prompts for language models

Abstract

The technology employs soft knowledge prompts (KPs) to inject relevant world knowledge into language models. This includes training KPs via self-supervised learning on data from one or more knowledge bases. KPs are task independent and can function as an external memory of the language models. KPs may be entity-centric, meaning that each prompt primarily encodes information about one entity from a given knowledge base. A method includes identifying a KP in response to a received input text, concatenating that KP to a sequence of word embeddings of the input text, applying the concatenated information to a trained language model, predicting an object entity name, computing a cross-entropy loss, and updating the identified KP based on the computed cross-entropy loss.