Systems And Methods For Automated Active Learning For Re-Training Of A Machine Learming Model

A computing device, comprising:

• a processor;
• and
• a non-transitory computer-readable medium having stored thereon instructions that, when executed by the processor, cause the processor to perform operations including:
• obtaining historical data including a plurality of data objects,
• performing, by a large language model (LLM), inferencing on the historical data resulting in generation of probabilistic labels including a probabilistic label for each of the plurality of data objects, wherein a first probabilistic label indicates whether a first data object is suspicious or benign,
• determining a subset of the data objects with probabilistic labels having lowest confidence scores associated therewith,
• generating a graphical user interface (GUI) that displays the subset of the data objects,
• receiving user input indicating a label for each of the subset of the data objects, and
• when a percentage of the probabilistic labels that deviates from labels comprising the user input meets or exceeds a threshold percentage, initiating a re-training process on the LLM.

A non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, cause the one or more processor to perform operations including:

• obtaining historical data including a plurality of data objects;
• performing, by a large language model (LLM), inferencing on the historical data resulting in generation of probabilistic labels including a probabilistic label for each of the plurality of data objects, wherein a first probabilistic label indicates whether a first data object is suspicious or benign;
• determining a subset of the data objects with probabilistic labels having lowest confidence scores associated therewith;
• generating a graphical user interface (GUI) that displays the subset of the data objects;
• receiving user input indicating a label for each of the subset of the data objects;
• when a percentage of the probabilistic labels that deviates from labels comprising the user input meets or exceeds a threshold percentage, initiating a re-training process on the LLM.