System for Automatically Evaluating the Output of Machine-Learned Models

A computing system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media that store instructions wherein, when executed by the one or more processors, the instructions cause the one or more processors to perform operations, the operations comprising:
• receiving an input query;
• processing the input query with a generative model to generate a model output based on the input query, wherein the input query comprises textual content, and wherein one or both of the input query and the output comprise one or more media elements;
• identifying, by the computing system, one or more representative subsequences that correspond to a representation based on the textual response;
• generating a plurality of tuple pairs based on the one or more representative subsequences that correspond to a representation and the one or more media elements;
• for each of one or more relevant tuple pairs in the plurality of tuple pairs, processing the respective tuple pair with an entailment-scoring machine-learned model to generate an entailment score for the respective tuple pair;
• and
• providing an entailment output for the model output based on the respective entailment scores generated for the one or more relevant tuple pairs.