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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.


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


A. Catastrophic forgetting


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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Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

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