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22年EMNLP的Calibrating Factual Knowledge in Pretrained Language Models的做法似乎也是在FFN层添加额外神经元,T-Patcher文中并没有与之进行对比。
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Same question for me. 后来我也思考了这个问题,我发现本文技术上主要的改进在于损失函数的设计。CALINET仅仅考虑了普通的损失,本文考虑了reliability和locality方面的损失(我觉得如果能够加上考虑generality会更好)。
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22年EMNLP的Calibrating Factual Knowledge in Pretrained Language Models的做法似乎也是在FFN层添加额外神经元,T-Patcher文中并没有与之进行对比。
The text was updated successfully, but these errors were encountered: