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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Implications of Minimum Description Length for Adversarial Attack in Natural Language Processing.

Kshitiz Tiwari1, Lu Zhang1

  • 1Department of Electrical Engineering and Computer Science, University of Arkansas, Fayetteville, AR 72701, USA.

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Summary
This summary is machine-generated.

This study introduces a new method for robust natural language processing (NLP) by analyzing adversarial attacks as causal mechanisms. It quantifies text alterations using algorithmic information, aiding in detecting manipulated data without original text.

Keywords:
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Area of Science:

  • Natural Language Processing
  • Causality in Machine Learning
  • Algorithmic Information Theory

Background:

  • Current methods for training robust NLP models face challenges in lexicon identification and multi-environment data acquisition.
  • Adversarial attacks pose a significant threat to the reliability of NLP systems.
  • Understanding the causal mechanisms behind these attacks is crucial for developing effective defenses.

Purpose of the Study:

  • To propose a novel approach for enhancing the robustness of natural language processing (NLP) models.
  • To investigate the causal mechanisms underlying adversarial attacks on NLP models.
  • To develop techniques for detecting text alterations caused by attacks, even without access to original data.

Main Methods:

  • Treating adversarial attack behavior as a complex causal mechanism.
  • Quantifying the algorithmic information of text alterations using the minimum description length (MDL) framework.
  • Employing masked language modeling (MLM) to measure the 'effort' of text transformation.

Main Results:

  • Developed a method to measure text alteration using MLM and MDL.
  • Demonstrated the ability to identify altered tokens based on their algorithmic information.
  • Established a technique for detecting adversarial manipulations without requiring original text data.

Conclusions:

  • The proposed causal approach offers a new perspective on robust NLP.
  • Algorithmic information, measured by MDL and MLM, provides a viable metric for detecting adversarial text modifications.
  • This method enhances NLP model robustness by enabling detection of altered data in challenging scenarios.