@inproceedings{wang-etal-2026-editing,
title = "Editing Rules in {LLM}s Requires More Than a Localized Update",
author = "Wang, Yating and
Zhao, Wenting and
Zhao, Yaqi and
Gong, Yongshun and
Yin, Yilong and
Sun, Haoliang",
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.421/",
pages = "9634--9650",
ISBN = "979-8-89176-459-0",
abstract = "Model editing aims to update large language models (LLMs) with new knowledge without full retraining, but existing methods mainly focus on fact-level knowledge. Rules that support mathematical and scientific reasoning must remain consistent across formulas, descriptions, and instances, making localized updates less effective for rule-level editing. In this paper, we construct RuleEdit-200, a manually verified benchmark of 200 mathematical and physical rules, each instantiated in formula, description, and instance forms. Using this benchmark, we conduct fine-grained causal tracing on four LLMs and identify a form-specific layer-wise separation of rule knowledge: formulas and descriptions are associated with earlier layers, whereas instances rely on a distinct set of middle-layer representations. This finding suggests that rule-level editing requires separate updates for different forms. Based on this insight, we propose Distributed Multi-Layer Editing (DMLE), which updates formulas, descriptions and instances at their corresponding layer ranges. Experiments on RuleEdit-200 show that DMLE remains competitive on standard editing metrics while substantially improving rule-level editing, with average gains of 18.22{\%} and 61.00{\%} in instance portability and rule understanding over the best baseline on each model."
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<abstract>Model editing aims to update large language models (LLMs) with new knowledge without full retraining, but existing methods mainly focus on fact-level knowledge. Rules that support mathematical and scientific reasoning must remain consistent across formulas, descriptions, and instances, making localized updates less effective for rule-level editing. In this paper, we construct RuleEdit-200, a manually verified benchmark of 200 mathematical and physical rules, each instantiated in formula, description, and instance forms. Using this benchmark, we conduct fine-grained causal tracing on four LLMs and identify a form-specific layer-wise separation of rule knowledge: formulas and descriptions are associated with earlier layers, whereas instances rely on a distinct set of middle-layer representations. This finding suggests that rule-level editing requires separate updates for different forms. Based on this insight, we propose Distributed Multi-Layer Editing (DMLE), which updates formulas, descriptions and instances at their corresponding layer ranges. Experiments on RuleEdit-200 show that DMLE remains competitive on standard editing metrics while substantially improving rule-level editing, with average gains of 18.22% and 61.00% in instance portability and rule understanding over the best baseline on each model.</abstract>
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%0 Conference Proceedings
%T Editing Rules in LLMs Requires More Than a Localized Update
%A Wang, Yating
%A Zhao, Wenting
%A Zhao, Yaqi
%A Gong, Yongshun
%A Yin, Yilong
%A Sun, Haoliang
%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 wang-etal-2026-editing
%X Model editing aims to update large language models (LLMs) with new knowledge without full retraining, but existing methods mainly focus on fact-level knowledge. Rules that support mathematical and scientific reasoning must remain consistent across formulas, descriptions, and instances, making localized updates less effective for rule-level editing. In this paper, we construct RuleEdit-200, a manually verified benchmark of 200 mathematical and physical rules, each instantiated in formula, description, and instance forms. Using this benchmark, we conduct fine-grained causal tracing on four LLMs and identify a form-specific layer-wise separation of rule knowledge: formulas and descriptions are associated with earlier layers, whereas instances rely on a distinct set of middle-layer representations. This finding suggests that rule-level editing requires separate updates for different forms. Based on this insight, we propose Distributed Multi-Layer Editing (DMLE), which updates formulas, descriptions and instances at their corresponding layer ranges. Experiments on RuleEdit-200 show that DMLE remains competitive on standard editing metrics while substantially improving rule-level editing, with average gains of 18.22% and 61.00% in instance portability and rule understanding over the best baseline on each model.
%U https://aclanthology.org/2026.emnlp-main.421/
%P 9634-9650
Markdown (Informal)
[Editing Rules in LLMs Requires More Than a Localized Update](https://aclanthology.org/2026.emnlp-main.421/) (Wang et al., EMNLP 2026)
ACL
- Yating Wang, Wenting Zhao, Yaqi Zhao, Yongshun Gong, Yilong Yin, and Haoliang Sun. 2026. Editing Rules in LLMs Requires More Than a Localized Update. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 9634–9650, Budapest, Hungary. Association for Computational Linguistics.