Abstract

Techniques for updating a large language model (LLM) to correct generation of undesired responses, such as incorrect outputs, toxic outputs, etc. are described. Typical methods of retraining and fine-tuning are inefficient and computationally expensive for LLMs. Some embodiments of the present disclosure involve identifying a salient layer of the LLM that is responsible for the undesired response and editing only the salient layer. This layer is identified by computing a saliency value for the layer using a mean of gradient values for the layer, and the layer with the greatest saliency value is selected for editing. For editing, a small network is used to update the weights of the selected layer. The LLM is updated to include the edited layer, and the updated LLM is used for future processing.

Date

October 28, 2025

Authors

K Mishra, T Soliman, A Galstyan, A Kumar, AK Ramakrishna

Inventors

Kshitij Mishra, Tamer Soliman, Aram Galstyan, Anoop Kumar, Anil K Ramakrishna

Patent_office

US

Patent_number

12456020

Application_number

18461143