Consecutive Batch Model Editing with HooK Layers

Shuaiyi Li, Yang Deng, Deng Cai, Hongyuan Lu, Liang Chen, Wai Lam


Abstract
As the typical retraining paradigm is unacceptably time- and resource-consuming, researchers are turning to model editing to find an effective way that supports both consecutive and batch scenarios to edit the model behavior directly. Despite all these practical expectations, existing model editing methods fail to realize all of them. Furthermore, the memory demands for such sequential model editing approaches tend to be prohibitive, frequently necessitating an external memory that grows incrementally over time. To cope with these challenges, we propose CoachHooK, a model editing method that simultaneously supports sequential and batch editing. CoachHooK is memory-friendly as it only needs a small amount of it to store several hook layers whose size remains unchanged over time. Experimental results demonstrate the superiority of our method over other batch-supportive model editing methods under both single-round and consecutive batch editing scenarios. Extensive analyses of CoachHooK have been conducted to verify the stability of our method over a number of consecutive steps.
Anthology ID:
2024.emnlp-main.765
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13817–13833
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.emnlp-main.765/
DOI:
10.18653/v1/2024.emnlp-main.765
Bibkey:
Cite (ACL):
Shuaiyi Li, Yang Deng, Deng Cai, Hongyuan Lu, Liang Chen, and Wai Lam. 2024. Consecutive Batch Model Editing with HooK Layers. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 13817–13833, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Consecutive Batch Model Editing with HooK Layers (Li et al., EMNLP 2024)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.emnlp-main.765.pdf