@inproceedings{zhai-etal-2026-prism,
title = "{PRISM}: Refracting Large-Batch Model Editing through Jacobian-Inferred {FFN} Subspaces",
author = "Zhai, Ziyue and
Liu, Qiyao and
Song, Hao and
Yu, Heng and
Lin, Guozhi and
Yang, Yulai and
Zhang, Xuan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.997/",
pages = "22931--22949",
ISBN = "979-8-89176-459-0",
abstract = "Model editing updates factual knowledge in large language models (LLMs) without retraining, but simultaneous large-batch edits can degrade generalization and locality as edit interactions accumulate. We study this problem through a multi-scale analysis of feed-forward network (FFN) and multi-head self-attention (MHSA) geometry. Weak FFN{--}MHSA token-level alignment can coexist with non-trivial distributional coupling, while Jacobian-inferred FFN local-response geometry provides a distinct proxy for locally supported response directions. Based on this distinction, we propose \textbf{PRISM}, which replaces unconstrained residual-target fitting with controlled residual correction within edit-conditioned, Jacobian-inferred local-response subspaces. On the standard CounterFact large-batch benchmark, improves the baseline{'}s Score by 1.3{--}1.7 points and Generalization by 1.7{--}1.8 points at 10k edits, with larger Generalization gains in beyond-10k scaling tests at 20k edits while maintaining competitive Specificity."
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%0 Conference Proceedings
%T PRISM: Refracting Large-Batch Model Editing through Jacobian-Inferred FFN Subspaces
%A Zhai, Ziyue
%A Liu, Qiyao
%A Song, Hao
%A Yu, Heng
%A Lin, Guozhi
%A Yang, Yulai
%A Zhang, Xuan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhai-etal-2026-prism
%X Model editing updates factual knowledge in large language models (LLMs) without retraining, but simultaneous large-batch edits can degrade generalization and locality as edit interactions accumulate. We study this problem through a multi-scale analysis of feed-forward network (FFN) and multi-head self-attention (MHSA) geometry. Weak FFN–MHSA token-level alignment can coexist with non-trivial distributional coupling, while Jacobian-inferred FFN local-response geometry provides a distinct proxy for locally supported response directions. Based on this distinction, we propose PRISM, which replaces unconstrained residual-target fitting with controlled residual correction within edit-conditioned, Jacobian-inferred local-response subspaces. On the standard CounterFact large-batch benchmark, improves the baseline’s Score by 1.3–1.7 points and Generalization by 1.7–1.8 points at 10k edits, with larger Generalization gains in beyond-10k scaling tests at 20k edits while maintaining competitive Specificity.
%U https://aclanthology.org/2026.emnlp-main.997/
%P 22931-22949
Markdown (Informal)
[PRISM: Refracting Large-Batch Model Editing through Jacobian-Inferred FFN Subspaces](https://aclanthology.org/2026.emnlp-main.997/) (Zhai et al., EMNLP 2026)
ACL