Lifelong model editing in large language models: Balancing low-cost targeted edits and catastrophic forgetting

Large language models (LLMs) are profoundly useful for a vast array of difficult tasks. But they sometimes make unpredictable
Deep Learning, NLP, NMT, AI, ML
Large language models (LLMs) are profoundly useful for a vast array of difficult tasks. But they sometimes make unpredictable