Enhancing AI with Adversarial Techniques
Transforming translation through innovative adversarial sample generation and testing for robustness.
About Our Work
Specializing in generating and testing adversarial samples for advanced AI models to enhance translation accuracy and robustness.
Sample Generation
Crafting semantic-preserving perturbations for AI models to enhance security and performance.
Robustness Testing
Assessing model resilience against adversarial samples to enhance performance.
Transferability Study
Evaluating cross-model vulnerabilities to improve overall AI robustness and security.
This work will advance collective understanding in three ways:
Model Transparency: By mapping GPT-4’s failure modes, we reveal latent vulnerabilities that could inform safer model architectures or deployment practices (e.g., input sanitization).
Robustness Benchmarks: Establishing standardized metrics for adversarial susceptibility in LLMs, enabling comparative studies across models.
Mitigation Blueprint: If fine-tuning proves effective, this could guide OpenAI (and others) to adopt adversarial training as a default step for high-stakes applications (e.g., medical or legal uses).
Societally, the project highlights risks of over-reliance on black-box AI systems and proposes actionable improvements. For OpenAI, insights could directly enhance GPT-4’s safety protocols or inspire new research directions (e.g., hybrid interpretable/black-box systems).