@inproceedings{zeng-etal-2026-hyperedit,
title = "{H}yper{E}dit: Unlocking Instruction-based Text Editing in {LLM}s via Hypernetworks",
author = "Zeng, Yiming and
Cao, Jinghan and
Li, Zexin and
Yu, Wanhao and
Ye, Zhankai and
Xiang, Dawei and
Hua, Ting and
Liu, Xin and
Gao, Shangqian and
Yu, Tingting",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.22/",
pages = "466--480",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models (LLMs) have fundamentally transformed natural language processing (NLP), demonstrating remarkable capabilities across a wide spectrum of tasks. However, when applied to instruction-based text editing, LLMs continue to exhibit some limitations. Different from free-form generation, instruction-based editing requires precise, targeted modifications that respect two essential properties: faithfully implementing the specific instruction and local fidelity. Existing approaches often overlook these properties, treating editing as a generic text generation problem. As a result, they either over-edit or fail to apply modifications consistently. To address this gap, we propose HyperEdit, a framework that adaptively processes each editing request to best align with it. To achieve this, HyperEdit generates request-specific dynamic weights that guide the editing process. The computational overhead of producing these weights is minimized through a carefully designed hypernetwork. With this design, HyperEdit achieves a relatively 9{\%} improvement over the state-of-the-art editing model."
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<abstract>Large Language Models (LLMs) have fundamentally transformed natural language processing (NLP), demonstrating remarkable capabilities across a wide spectrum of tasks. However, when applied to instruction-based text editing, LLMs continue to exhibit some limitations. Different from free-form generation, instruction-based editing requires precise, targeted modifications that respect two essential properties: faithfully implementing the specific instruction and local fidelity. Existing approaches often overlook these properties, treating editing as a generic text generation problem. As a result, they either over-edit or fail to apply modifications consistently. To address this gap, we propose HyperEdit, a framework that adaptively processes each editing request to best align with it. To achieve this, HyperEdit generates request-specific dynamic weights that guide the editing process. The computational overhead of producing these weights is minimized through a carefully designed hypernetwork. With this design, HyperEdit achieves a relatively 9% improvement over the state-of-the-art editing model.</abstract>
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%0 Conference Proceedings
%T HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks
%A Zeng, Yiming
%A Cao, Jinghan
%A Li, Zexin
%A Yu, Wanhao
%A Ye, Zhankai
%A Xiang, Dawei
%A Hua, Ting
%A Liu, Xin
%A Gao, Shangqian
%A Yu, Tingting
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zeng-etal-2026-hyperedit
%X Large Language Models (LLMs) have fundamentally transformed natural language processing (NLP), demonstrating remarkable capabilities across a wide spectrum of tasks. However, when applied to instruction-based text editing, LLMs continue to exhibit some limitations. Different from free-form generation, instruction-based editing requires precise, targeted modifications that respect two essential properties: faithfully implementing the specific instruction and local fidelity. Existing approaches often overlook these properties, treating editing as a generic text generation problem. As a result, they either over-edit or fail to apply modifications consistently. To address this gap, we propose HyperEdit, a framework that adaptively processes each editing request to best align with it. To achieve this, HyperEdit generates request-specific dynamic weights that guide the editing process. The computational overhead of producing these weights is minimized through a carefully designed hypernetwork. With this design, HyperEdit achieves a relatively 9% improvement over the state-of-the-art editing model.
%U https://aclanthology.org/2026.findings-acl.22/
%P 466-480
Markdown (Informal)
[HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks](https://aclanthology.org/2026.findings-acl.22/) (Zeng et al., Findings 2026)
ACL
- Yiming Zeng, Jinghan Cao, Zexin Li, Wanhao Yu, Zhankai Ye, Dawei Xiang, Ting Hua, Xin Liu, Shangqian Gao, and Tingting Yu. 2026. HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks. In Findings of the Association for Computational Linguistics: ACL 2026, pages 466–480, San Diego, California, United States. Association for Computational Linguistics.