Reference and training data validation for machine learning models

Abstract

Systems and methods are provided for a prompt and content generation service to validate generated references and training data sets of large language models (LLMs). The prompt and content generation service may determine if a reference generated by an LLM is valid by searching a training dataset for tokens using a prompt, generated answer, and generated reference for content and references in the training data set. If the prompt and content generation service finds content or a valid reference with a certain confidence level, the prompt and content generation service may indicate that the generated content and reference is valid. The prompt and content generation service may additionally validate a saved checkpoint during training of an LLM by executing use case scenarios against the LLM checkpoint and determining if expected answers match generated answers.