Papers by Maochuan Lu
Efficient Knowledge Editing via Minimal Precomputation (2025.acl-short)
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| Challenge: | Knowledge editing methods like MEMIT require a one-time but significant computational cost. |
| Approach: | They propose to pre-compute 44 million hidden vectors per edited layer . authors show that this precomputation step is unnecessary . |
| Outcome: | The proposed methods can be performed by pre-computing a small portion of 44 million hidden vectors. |
Lifelong Knowledge Editing requires Better Regularization (2025.findings-emnlp)
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Akshat Gupta, Phudish Prateepamornkul, Maochuan Lu, Ahmed Alaa, Thomas Hartvigsen, Gopala Anumanchipalli
| Challenge: | Knowledge editing is a promising way to improve factuality in large language models, but recent studies have shown significant model degradation during sequential editing. |
| Approach: | They formalize locate-then-edit methods as a two-step fine-tuning process . they show that model degradation occurs due to over-optimization of internal activations . |
| Outcome: | The proposed methods reduce time and improve factuality by 42-61%. |