Systems and methods for shared latent space prompt tuning

A system of shared prompt-tuning in language models, the system comprising:

• a data interface that receives a training dataset of instances;
• a memory storing an instance encoder, a skill vector generator, a prompt generator and a pretrained language model, and a plurality of processor-executable instructions;
• and
• one or more processors reading and executing the plurality of processor-executable instructions to perform:
• encoding, by the instance encoder, a first instance from the training dataset into a first embedding in a latent space;
• querying a skill latent space on the first embedding to obtain a first skill vector;
• transforming, by the prompt generator, the first skill vector into a set of prompt tokens;
• generating, by the pretrained language model, an answer in response to an input of the first instance prepended with the prompt tokens;
• computing a training objective based on the generated answer conditioned on the prompt generator and the first instance;
• and
• updating the prompt generator based on the training objective via backpropagation while the pre-trained language model and the instance encoder are frozen.

A non-transitory processor-readable storage medium storing a plurality of processor-executable instructions for shared prompt-tuning in language models, the instructions being executed by one or more processors to perform operations comprising:

• receiving, via a data interface, a training dataset of instances;
• encoding, by an instance encoder, a first instance from the training dataset into a first embedding in a latent space;
• querying a skill latent space on the first embedding to obtain a first skill vector;
• transforming, by a prompt generator, the first skill vector into a set of prompt tokens;
• generating, by a pretrained language model, an answer in response to an input of the first instance prepended with the prompt tokens;
• computing a training objective based on the generated answer conditioned on the prompt generator and the first instance;
• and
• updating the prompt generator based on the training objective via backpropagation while the pre-trained language model and the instance encoder are frozen.