Comprehensive Investigation
Comprehensive investigations across diverse scientific domains are currently focused on improving the robustness, fairness, and efficiency of machine learning models. Research emphasizes addressing biases in models, particularly concerning race and gender, and enhancing their generalizability across different datasets and applications, often employing techniques like domain adaptation and data augmentation. These efforts are crucial for ensuring the reliability and ethical deployment of AI in various fields, ranging from healthcare and social media analysis to industrial automation and natural language processing. The ultimate goal is to develop more accurate, trustworthy, and equitable AI systems.
Papers
Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context
Nilanjana Das, Edward Raff, Manas Gaur
A Thorough Investigation into the Application of Deep CNN for Enhancing Natural Language Processing Capabilities
Chang Weng, Scott Rood, Mehdi Ali Ramezani, Amir Aslani, Reza Zarrab, Wang Zwuo, Sanjeev Salimans, Tim Satheesh