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ZhenHua Ling

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TIST Journal 2026 Journal Article

Reliable Batch-Sequential Model Editing via Enhanced Editing Anchor Compression

  • Haoxiang Xu
  • Ziqi Peng
  • Xiaoyu Wang
  • Hanjie Guo
  • ZhenHua Ling

Large language models (LLMs) often suffer from outdated or incorrect knowledge, prompting ongoing research into efficient model editing. Existing methods, however, mainly target individual knowledge facts. When multiple facts need to be edited in a coherent sequence, they frequently lead to deviations or even breakdowns in model's general abilities. This problem intensifies in batch-sequential editing, where multiple facts are updated simultaneously, compared to single-sequential editing. In this work, by analyzing the parameter matrix, we identify that the degradation stems from unintended modifications that should ideally remain unaffected. These changes accumulate with the number and batch size of edits, ultimately harming editing performance and general abilities. To address this, we propose B atch-Aware E diting A nchor C ompression (B-EAC), a framework tailored for sequential model editing. B-EAC dynamically selects essential anchors for each edit while compressing the influence on nearby parameters. It adopts a layer-wise anchor selection strategy to prevent anchor conflicts during concurrent edits and introduces a rolling anchor refresh mechanism to enhance adaptability across batches. Experiments conducted on three LLMs across four tasks demonstrate that B-EAC effectively suppresses deviation during model editing, achieving a 36.54% performance improvement compared to the case without it. Our work offers a practical and theoretically grounded framework for updating LLMs efficiently, paving the way for continual knowledge refinement in real-world applications.

Details DOI

IJCAI Conference 2016 Conference Paper

Distraction-Based Neural Networks for Modeling Document

  • Qian Chen
  • Xiaodan Zhu
  • ZhenHua Ling
  • Si Wei
  • Hui Jiang

Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences. Whether and how such an approach can be extended to help model larger spans of text, e. g. , documents, is intriguing, and further investigation would still be desirable. This paper aims to enhance neural network models for such a purpose. A typical problem of document-level modeling is automatic summarization, which aims to model documents in order to generate summaries. In this paper, we propose neural models to train computers not just to pay attention to specific regions and content of input documents with attention models, but also distract them to traverse between different content of a document so as to better grasp the overall meaning for summarization. Without engineering any features, we train the models on two large datasets. The models achieve the state-of-the-art performance, and they significantly benefit from the distraction modeling, particularly when input documents are long.

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v2026.09.13