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From adversarial arms race to model-centric evaluation motivating a unified automatic robustness evaluation framework
Chen, Yangyi
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https://hdl.handle.net/2142/125641
Description
- Title
- From adversarial arms race to model-centric evaluation motivating a unified automatic robustness evaluation framework
- Author(s)
- Chen, Yangyi
- Issue Date
- 2024-07-17
- Director of Research (if dissertation) or Advisor (if thesis)
- Ji, Heng
- Department of Study
- Siebel Computing &DataScience
- Discipline
- Computer Science
- Degree Granting Institution
- University of Illinois at Urbana-Champaign
- Degree Name
- M.S.
- Degree Level
- Thesis
- Keyword(s)
- Robustness
- Evaluation
- Abstract
- Textual adversarial attacks can discover models' weaknesses by adding semantic-preserved but misleading perturbations to the inputs. The long-lasting adversarial attack-and-defense arms race in Natural Language Processing (NLP) is algorithm-centric, providing valuable techniques for automatic robustness evaluation. However, the existing practice of robustness evaluation may exhibit issues of incomprehensive evaluation, impractical evaluation protocol, and invalid adversarial samples. In this paper, we aim to set up a unified automatic robustness evaluation framework, shifting towards model-centric evaluation to further exploit the advantages of adversarial attacks. To address the above challenges, we first determine robustness evaluation dimensions based on model capabilities and specify the reasonable algorithm to generate adversarial samples for each dimension. Then we establish the evaluation protocol, including evaluation settings and metrics, under realistic demands. Finally, we use the perturbation degree of adversarial samples to control the sample validity. We implement a toolkit RobTest that realizes our automatic robustness evaluation framework. In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework.
- Graduation Semester
- 2024-08
- Type of Resource
- Thesis
- Handle URL
- https://hdl.handle.net/2142/125641
- Copyright and License Information
- Copyright 2024 Yangyi Chen
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Graduate Dissertations and Theses at Illinois PRIMARY
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