Unverified50% confidenceFactExact time
The mainstream strategies for addressing catastrophic forgetting fall into three categories: regularization methods (such as EWC), dynamic architecture methods (such as Progressive Neural Networks), and replay methods.
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Long-term
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9/12/2026
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Unverified研究者已提出弹性权重巩固(EWC)、渐进式神经网络等缓解灾难性遗忘的方案,但在超大规模模型上的有效性仍是活跃研究方向75% similarUnverified学术界提出了 EWC(Elastic Weight Consolidation)、渐进式网络等方法试图缓解灾难性遗忘,但在大规模语言模型上的应用仍处于早期探索阶段67% similarUnverifiedHaving the model think before acting (such as Chain-of-Thought) can significantly reduce error rates, a point supported by research from OpenAI and Anthropic.64% similarUnverifiedParameterized policy updates directly modify model weights (e.g., online updating of LoRA fine-tuning layers), offering a higher capability ceiling but introducing the risk of catastrophic forgetting; non-parameterized approaches change behavior by modifying prompts, retrieved content, or tool configurations, offering strong reversibility but limited expressiveness.63% similarUnverifiedUncensored models can be achieved through three technical approaches: fine-tuning, system prompts, and activation engineering.63% similar
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