Model editing of a tabular search large language model using disagreement over out of distribution samples via transductive learning and contextual bandits

Abstract

A method for updating a tabular search large language model (LLM) includes performing data pre-processing on new data associated with the tabular dataset to obtain a set of sequences, applying a first fine-tuning operation on the tabular search LLM using the set of sequences, applying a second fine-tuning operation on the tabular search LLM using training data to obtain a set of final loss results and a set of updatable gradients, wherein the training data comprises at least the set of sentence predictions, applying an optimization function on the set of final loss results and the set of updatable gradients to obtain optimized gradient descent parameters, and applying the updated tabular search LLM to a new input associated with the new data to produce a new output.