AI BriefingReducing Forgetting Through Knowledge Editing
Key point
A parameter selection technique has been proposed that reduces forgetting of existing knowledge while training only on new data.
Details
Researchers proposed a method to reduce catastrophic forgetting, where existing knowledge is damaged when learning new knowledge during continued pretraining.
The core idea is a selective knowledge suppression approach: after identifying parameters related to a specific concept, only the parameters unlikely to affect existing knowledge are updated, while the rest are frozen.
Previously, a widely used method to prevent forgetting was mixing new data with past data for training, but this significantly increases costs since it requires retraining on the entire dataset. The proposed method aims to preserve existing knowledge and capabilities while training only on new data.
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