HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks

• URL: http://arxiv.org/abs/2512.12544v1
• Date: Sun, 14 Dec 2025 04:28:39 GMT
• Title: HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks
• Authors: Yiming Zeng, Jinghan Cao, Zexin Li, Wanhao Yu, Zhankai Ye, Dawei Xiang, Ting Hua, Xin Liu, Shangqian Gao, Tingting Yu,
• Abstract summary: We introduce hypernetwork-based dynamic adaptation that generates request-specific parameters.<n>We develop difference-aware regularization that focuses supervision on modified spans, preventing over-editing.<n>HyperEdit achieves a 9%--30% relative improvement in BLEU on modified regions over state-of-the-art baselines.
• Score: 19.648438719273024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instruction-based text editing is increasingly critical for real-world applications such as code editors (e.g., Cursor), but Large Language Models (LLMs) continue to struggle with this task. Unlike free-form generation, editing requires faithfully implementing user instructions while preserving unchanged content, as even minor unintended modifications can break functionality. Existing approaches treat editing as generic text generation, leading to two key failures: they struggle to faithfully align edits with diverse user intents, and they often over-edit unchanged regions. We propose HyperEdit to address both issues. First, we introduce hypernetwork-based dynamic adaptation that generates request-specific parameters, enabling the model to tailor its editing strategy to each instruction. Second, we develop difference-aware regularization that focuses supervision on modified spans, preventing over-editing while ensuring precise, minimal changes. HyperEdit achieves a 9%--30% relative improvement in BLEU on modified regions over state-of-the-art baselines, despite utilizing only 3B parameters.

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