Representation Forgetting

Representation forgetting describes the phenomenon where neural networks, particularly large language models, lose or alter previously learned internal representations during continual learning, impacting their performance on past tasks. Current research focuses on understanding the mechanisms driving this forgetting, developing methods to mitigate it (e.g., through representation steering or adaptive unlearning techniques), and evaluating its impact on model generalization and knowledge retention across diverse tasks and architectures. These efforts are crucial for advancing continual learning, enabling more robust and efficient AI systems capable of adapting to evolving data streams and avoiding the need for complete retraining.