What is catastrophic interference in continual learning for NLP?

It is the loss of previously learned knowledge when training on new tasks.

Neural networks overwrite weights when trained sequentially on new tasks. This leads to forgetting old knowledge. Techniques like Elastic Weight Consolidation and rehearsal buffers mitigate this by preserving important parameters or replaying old data.