Lifelong Sequential Knowledge Editing without Model Degradation
Prior work in parameter-modifying knowledge editing has shown that
large-scale sequential editing leads to significant model degradation. In this
paper, we study the reasons behind this and scale sequential knowledge editing
to 10,000 sequential edits, while maintaining the downstream performance of the
original model. We first show that locate-then-edit knowledge editing methods
lead to overfitting on the edited facts. We also show that continuous knowledge
editing using these methods leads to disproportionate growth in the norm of the
edited matrix. We then provide a crucial insight into the inner workings of
locate-then-edit methods. We show that norm-growth is a hidden trick employed
by these methods that gives larger importance to the output activations
produced from the edited layers. With this "importance hacking", the edited
layers provide a much larger contributions to the model's output. To mitigate
these issues, we present ENCORE - Early stopping and Norm-Constrained Robust
knowledge Editing. ENCORE controls for overfitting and the disproportionate
norm-growth to enable long-term sequential editing, where we are able to
perform up to 10,000 sequential edits without loss of downstream performance.
ENCORE is also 61% faster than MEMIT and 64% faster than AlphaEdit on
Llama3-8B.
67a1aa5dc7fa0ccf0a32ceb1
Akshat Gupta
extracted_confirmed
2025-02-04T05:53:11.213Z
64e8f4a24f3f7b0b84834315
/avatars/242bb68c7ccffe5061c2d1c229ea3b0b.svg
Akshat Gupta
user
akshat57
67a1aa5dc7fa0ccf0a32ceb2
Phudish Prateepamornkul
67a1aa5dc7fa0ccf0a32ceb3
Maochuan Lu
67a1aa5dc7fa0ccf0a32ceb4
Ahmed Alaa
claimed_verified
2025-02-07T09:58:25.997Z
6353668fc0b9d81cd2668a2c
/avatars/4f6e54e702945d7d58b933ba4115a3e0.svg
Ahmed Alaa
user
amalaa
67a1aa5dc7fa0ccf0a32ceb5
Thomas Hartvigsen
67a1aa5dc7fa0ccf0a32ceb6
Gopala Anumanchipalli
admin_assigned
2025-02-04T21:11:09.416Z
60523e4aa7226b25aaeea2b8
/avatars/316ca348da91ebced86991f36150c959.svg
Gopala Anumanchipalli
user
gopalakr
Lifelong Sequential Knowledge Editing without Model Degradation
Prior work in parameter-modifying knowledge editing has shown that
large-scale sequential editing leads to significant model degradation. In this
paper, we study the reasons behind this and scale sequential knowledge editing
to 10,000 sequential edits, while maintaining the downstream performance of the
original model. We first show that locate-then-edit knowledge editing methods
lead to overfitting on the edited facts. We also show that continuous knowledge
editing using these methods leads to disproportionate growth in the norm of the
edited matrix. We then provide a crucial insight into the inner workings of
locate-then-edit methods. We show that norm-growth is a hidden trick employed
by these methods that gives larger importance to the output activations
produced from the edited layers. With this "importance hacking", the edited
layers provide a much larger contributions to the model's output. To mitigate
these issues, we present ENCORE - Early stopping and Norm-Constrained Robust
knowledge Editing. ENCORE controls for overfitting and the disproportionate
norm-growth to enable long-term sequential editing, where we are able to
perform up to 10,000 sequential edits without loss of downstream performance.
ENCORE is also 61% faster than MEMIT and 64% faster than AlphaEdit on
Llama3-8B.
ENCORE, an early stopping and norm-constrained robust knowledge editing method, allows for up to 10,000 sequential edits without degrading downstream performance and improves speed compared to existing techniques.
locate-then-edit knowledge editing overfitting norm growth importance hacking ENCORE MEMIT AlphaEdit Llama3-8B