Sample broadening using llm-diff to expand active learning strategies

A method for implementing an active learning strategy on a machine learning model, the method comprising:

• receiving a plurality of unlabeled data samples;
• determining a plurality of seed cases;
• for each of the seed cases, identifying a subset of the unlabeled data samples that is semantically similar to the seed case;
• identifying one or more of the unlabeled data samples having a prediction score difference above a predetermined threshold, wherein the prediction score difference for a given unlabeled data sample is determined by comparing a prediction score of the given unlabeled data sample provided by a large language model (LLM) with a prediction score of the given unlabeled data sample provided by the machine learning model;
• annotating the subsets of the unlabeled data samples and the one or more of the unlabeled data samples to generate a labeled dataset;
• and
• training the machine learning model using the labeled dataset.

A non-transitory computer-readable medium storing a computer-readable program including instructions that, when executed by processing circuitry, causes the processing circuitry to:

• receive a plurality of unlabeled data samples;
• determine a plurality of seed cases;
• for each of the seed cases, identify a subset of the unlabeled data samples that is semantically similar to the seed case;
• identify one or more of the unlabeled data samples having a prediction score difference above a predetermined threshold, wherein the prediction score difference for a given unlabeled data sample is determined by comparing a prediction score of the given unlabeled data sample provided by a large language model (LLM) with a prediction score of the given unlabeled data sample provided by the machine learning model;
• annotate the subsets of the unlabeled data samples and the one or more of the unlabeled data samples to generate a labeled dataset;
• and
• train the machine learning model using the labeled dataset.