Retaining Knowledge in AI : Solving Catastrophic Forgetting in LLMs
Last Updated on August 29, 2025 by Editorial Team
Author(s): Sanket Rajaram
Originally published on Towards AI.
Part 1: The Learning Journey of a Kid in the School
Imagine a kid in school learning about basic arithmetic in one semester. By the next year, they move on to geometry and algebra, but in the process, they seem to forget how to add and subtract. Teachers must frequently re-teach old concepts because the kid struggles to retain prior knowledge while learning new skills.
The article discusses the issue of catastrophic forgetting in AI, drawing parallels with how children learn and forget concepts. It introduces methods such as Elastic Weight Consolidation (EWC), Replay Methods, and Parameter-Efficient Fine-Tuning (PEFT) to help AI systems retain foundational knowledge while acquiring new skills, likening these techniques to teaching methods used in schools.
Read the full blog for free on Medium.
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