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Related Experiment Video

Updated: Jul 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

AdaptiveWordBug: Generating adversarial texts with an adaptive scoring strategy against deep learning classifiers.

Yunting Zhang1, Lin Ye1, Baisong Li2

  • 1School of Cyberspace Science, Harbin Institute of Technology, Harbin, 150001, China.

Neural Networks : the Official Journal of the International Neural Network Society
|November 8, 2025
PubMed
Summary
This summary is machine-generated.

AdaptiveWordBug enhances adversarial text generation by combining multiple word importance scoring methods. This adaptive approach improves the effectiveness of attacks against deep learning models like BERT and ChatGPT.

Keywords:
Adversarial exampleDeep neural networkScoring methodText classificationTextual adversarial attack

Related Experiment Videos

Last Updated: Jul 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

  • Natural Language Processing
  • Machine Learning Security

Background:

  • Deep learning models are vulnerable to adversarial attacks.
  • Current word-level attack methods rely on single-model dependent scoring, limiting accuracy and comprehensiveness.
  • Effective adversarial text generation is crucial for developing robust defense mechanisms.

Purpose of the Study:

  • To propose a novel black-box adversarial text generation method for text classification.
  • To enhance the accuracy and comprehensiveness of word importance scoring in adversarial attacks.
  • To develop a versatile method adaptable to various target models.

Main Methods:

  • Introduced AdaptiveWordBug, a black-box adversarial text generation method.
  • Developed the Adaptive Scoring Strategy (ASS), combining four scoring approaches (three model-dependent, one model-independent).
  • Implemented adaptive parameters for each scoring method, adjustable per text.

Main Results:

  • AdaptiveWordBug demonstrated superior attack effectiveness compared to baseline methods.
  • The Adaptive Scoring Strategy accurately identified important words across different texts.
  • The method showed good suitability for diverse target models, including Chinese BERT and ChatGPT.

Conclusions:

  • AdaptiveWordBug significantly improves adversarial text generation effectiveness through its adaptive scoring strategy.
  • The proposed method offers a more comprehensive and accurate way to identify important words for perturbation.
  • AdaptiveWordBug provides a flexible and effective tool for evaluating and enhancing the robustness of text classification models.