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Creators/Authors contains: "Ekin, Tahir"

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  1. ; ; (, Data & Policy)
    Abstract The emergence of large language models has significantly expanded the use of natural language processing (NLP), even as it has heightened exposure to adversarial threats. We present an overview of adversarial NLP with an emphasis on challenges, policy implications, emerging areas, and future directions. First, we review attack methods and evaluate the vulnerabilities of popular NLP models. Then, we review defense strategies that include adversarial training. We describe major policy implications, identify key trends, and suggest future directions, such as the use of Bayesian methods to improve the security and robustness of NLP systems. 
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    Free, publicly-accessible full text available September 22, 2026
  2. ; (, IEEE, 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA))
    Free, publicly-accessible full text available August 7, 2026
  3. ; ; (, Proceedings of 58th Hawaii International Conference on System Sciences)
    Natural language processing (NLP) has gained wider utilization with the emergence of large language models. However, adversarial attacks threaten their reliability. We present an overview of adversarial NLP with an emphasis on challenges, emerging areas and future directions. First, we review attack methods and evaluate the vulnerabilities of popular NLP models. Then, we review defense strategies including adversarial training. We identify key trends and suggest future directions such as the use of Bayesian methods to improve the security and robustness of NLP systems. 
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