Machine learning techniques for validating and mutating outputs from predictive systems

An apparatus for processing predictive system output, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the processor, cause the apparatus to at least:

• receive predictive output data from a predictive system, the predictive output data comprising a plurality classification labels;
• generate an input sequence of tokens, the input sequence of tokens comprising a tokenization of the plurality of classification labels based at least in part on an index of predefined classification labels;
• generate, using a prediction validation machine learning framework, a mutation of the input sequence of tokens, wherein:
• the prediction validation machine learning framework comprises an embedding machine learning model and a mutation machine learning model, wherein:
• (i) the embedding machine learning model is configured to (a) generate, for each token of the input sequence of tokens, a token embedding, and (b) concatenate each token embedding into a token embedding matrix,
• (ii) the mutation machine learning model is configured to generate an output matrix, comprising an output sequence of tokens, based at least in part on the token embedding matrix,
• (iii) the mutation of the input sequence of tokens comprises retrieval of one or more output classification labels associated with the output sequence of tokens based at least in part on the index of predefined classification labels;
• and
• perform one or more prediction-based actions based at least in part on the mutation of the input sequence of tokens.

A computer program product for processing predictive system output, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to:

• receive predictive output data from a predictive system, the predictive output data comprising a plurality classification labels;
• generate an input sequence of tokens, the input sequence of tokens comprising a tokenization of the plurality of classification labels based at least in part on an index of predefined classification labels;
• generate, using a prediction validation machine learning framework, a mutation of the input sequence of tokens, wherein:
• the prediction validation machine learning framework comprises an embedding machine learning model and a mutation machine learning model, wherein:
• (i) the embedding machine learning model is configured to (a) generate, for each token of the input sequence of tokens, a token embedding, and (b) concatenate each token embedding into a token embedding matrix,
• (ii) the mutation machine learning model is configured to generate an output matrix, comprising an output sequence of tokens, based at least in part on the token embedding matrix,
• (iii) the mutation of the input sequence of tokens comprises retrieval of one or more output classification labels associated with the output sequence of tokens based at least in part on the index of predefined classification labels;
• and
• perform one or more prediction-based actions based at least in part on the mutation of the input sequence of tokens.