Retaining Knowledge in AI : Solving Catastrophic Forgetting in LLMs

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.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI