Large language model unlearning via loss adjustments

A method for implementing unlearning in large-language models (LLM) to enhance LLM performance, the method comprising:

• accessing, by a processor, forget data samples from one or more datasets;
• associating, by the processor, a template response for each forget data sample via implementation of one or more LLMs;
• and
• training, by the processor, a target LLM using a forget data only loss adjustment (FLAT) function to generate an unlearned LLM, including implementing a loss adjustment to maximize a divergence for between an available template answer and a forget answer only with respect to forget data samples.