Machine Unlearning in Large Language Models using a Neuroscientific Cognitive Approach

Optimizing Unlearning LLMs with advanced neuro-inspired algorithms

• Developed and improved a state-of-the-art unlearning algorithm for Large Language Models by adding extensive representations of the memory system, modulations of the attention mechanism, and other cognitive processes motivated by neuroscience.
• Created an adaptive threshold mechanism for the reconsolidation window to optimise unlearning timing and requirement depending on error severity, concept usage frequency, and emotional connotations.