Unlearning text data from trained large language models

A computer implemented method of unlearning text data from a trained large language model (LLM), comprising:

• accessing a trained LLM trained on a training dataset of text data;
• selecting a first set of text data for being retained and a second set of text data for being unlearned;
• computing a domain separation for the trained LLM to disentangle the representations of the first set of text data and the second text within a latent representation space of the trained LLM by adapting weights of a set of neurons of the trained LLM;
• and
• generating an adapted LLM from the trained LLM, the adapted LLM comprising the trained LLM with the adapted weights in response to the domain separation.

A method of unlearning text data from a trained large language model (LLM), comprising:

• using at least one processor executing a code for:
• receiving a trained LLM trained on a training dataset of text data stored on a data storage device, wherein the LLM is implemented as a neural network with a plurality of weights defined for a plurality of connected neurons;
• receiving a selection out of a first set of text data for being retained and a second set of text data for being unlearned;
• accessing memory locations of a memory storing weights of a set of neurons' connections of the trained LLM for adapting the stored weights for performing a domain separation on the trained LLM to disentangle the representations of a selected sub-set out of the first set of text data and the second set of text data, within a latent representation space of the trained LLM by;
• storing in the data storage device, an adapted LLM generated from the trained LLM, the adapted LLM comprising the trained LLM with the adapted weights in response to the domain separation;
• and
• accessing locations of the data storage device storing weights of the adapted LLM for adapting the stored weights for performing an unlearning process on the adapted LLM for unlearning of the second set of text data while maintaining inference performance on the first set of text data.