@inproceedings{jeong-etal-2026-klod,
title = "{KLOD}: Locality-Preserving Knowledge Editing via Non-Target Distribution Preservation",
author = "Jeong, Hojun and
Kim, Gyunyeop and
Kang, Sangwoo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1241/",
doi = "10.18653/2026.findings-emnlp.1241",
pages = "26264--26286",
ISBN = "979-8-89176-462-0",
abstract = "Fine-tuning-based knowledge editing is simple and architecture-agnostic, but standard cross-entropy increases the edited target probability without explicitly constraining changes in the non-target output distribution. In sequential editing, such unconstrained redistribution can accumulate as distributional drift and contribute to locality degradation. We propose KLOD, a bounded and distribution-preserving objective for fine-tuning-based knowledge editing that separates the intended target update from distributions that should remain stable. KLOD stops target amplification once a probability threshold is reached, while preserving the target-excluded non-target distribution at target positions and the full next-token distribution at prefix positions. Experiments on CounterFact and ZsRE with LLaMA3-8B-Instruct and Qwen2.5-7B-Instruct show that KLOD substantially mitigates locality degradation while maintaining high edit reliability. The target probability threshold further provides a controllable Generalization{--}Locality trade-off. Ablation, multi-seed, and distributional KL analyses support the interpretation that KLOD{'}s locality gains are associated with preserving output distributions rather than simply weakening the edit."
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<abstract>Fine-tuning-based knowledge editing is simple and architecture-agnostic, but standard cross-entropy increases the edited target probability without explicitly constraining changes in the non-target output distribution. In sequential editing, such unconstrained redistribution can accumulate as distributional drift and contribute to locality degradation. We propose KLOD, a bounded and distribution-preserving objective for fine-tuning-based knowledge editing that separates the intended target update from distributions that should remain stable. KLOD stops target amplification once a probability threshold is reached, while preserving the target-excluded non-target distribution at target positions and the full next-token distribution at prefix positions. Experiments on CounterFact and ZsRE with LLaMA3-8B-Instruct and Qwen2.5-7B-Instruct show that KLOD substantially mitigates locality degradation while maintaining high edit reliability. The target probability threshold further provides a controllable Generalization–Locality trade-off. Ablation, multi-seed, and distributional KL analyses support the interpretation that KLOD’s locality gains are associated with preserving output distributions rather than simply weakening the edit.</abstract>
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%0 Conference Proceedings
%T KLOD: Locality-Preserving Knowledge Editing via Non-Target Distribution Preservation
%A Jeong, Hojun
%A Kim, Gyunyeop
%A Kang, Sangwoo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F jeong-etal-2026-klod
%X Fine-tuning-based knowledge editing is simple and architecture-agnostic, but standard cross-entropy increases the edited target probability without explicitly constraining changes in the non-target output distribution. In sequential editing, such unconstrained redistribution can accumulate as distributional drift and contribute to locality degradation. We propose KLOD, a bounded and distribution-preserving objective for fine-tuning-based knowledge editing that separates the intended target update from distributions that should remain stable. KLOD stops target amplification once a probability threshold is reached, while preserving the target-excluded non-target distribution at target positions and the full next-token distribution at prefix positions. Experiments on CounterFact and ZsRE with LLaMA3-8B-Instruct and Qwen2.5-7B-Instruct show that KLOD substantially mitigates locality degradation while maintaining high edit reliability. The target probability threshold further provides a controllable Generalization–Locality trade-off. Ablation, multi-seed, and distributional KL analyses support the interpretation that KLOD’s locality gains are associated with preserving output distributions rather than simply weakening the edit.
%R 10.18653/2026.findings-emnlp.1241
%U https://aclanthology.org/2026.findings-emnlp.1241/
%U https://doi.org/10.18653/2026.findings-emnlp.1241
%P 26264-26286
Markdown (Informal)
[KLOD: Locality-Preserving Knowledge Editing via Non-Target Distribution Preservation](https://aclanthology.org/2026.findings-emnlp.1241/) (Jeong et al., Findings 2026)
ACL