Generative AI with LLMs: What is Catastrophic Forgetting in Neural Networks?

Learn the definition and causes of catastrophic forgetting in neural networks, and how it affects continual learning and model performance.

Question

Fine-tuning a model on a single task can improve model performance specifically on that task; however, it can also degrade the performance of other tasks as a side effect. This phenomenon is known as:

A. Catastrophic forgetting
B. Model toxicity
C. Instruction bias
D. Catastrophic loss

Answer

A. Catastrophic forgetting

Explanation

The correct answer is A. Catastrophic forgetting. Catastrophic forgetting is the tendency of an artificial neural network to abruptly and drastically forget previously learned information upon learning new information. This phenomenon is a major challenge for continual learning, where the model needs to learn new tasks without forgetting old ones. Fine-tuning a model on a single task can improve its performance on that task, but it can also overwrite the weights that are important for other tasks, resulting in a loss of generalization.

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