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Lifelong model editing in large language models: Balancing low-cost targeted edits and catastrophic forgetting
Microsoft Research www.microsoft.com
Lifelong model editing fixes mistakes discovered after model deployment. This work could expand sequential editing to model properties like fairness and privacy and enable a new class of solutions for adapting LLMs over long deployment lifetimes.
The post Lifelong model editing in large language models: Balancing low-cost targeted edits and catastrophic forgetting appeared first on Microsoft Research.
cost deployment editing fairness language language models large language large language models llms low mistakes model deployment privacy research blog solutions work