This AI Paper from the National University of Singapore Introduces a Defense Against Adversarial Attacks on LLMs Utilizing Self-Evaluation

Subject: Enhancing Safety and Reliability of Large Language Models (LLMs) At [Company Name], we understand the challenges in ensuring the safety and reliability of Large Language Models (LLMs). Adversarial attacks continue to pose a threat, requiring efficient and accessible solutions. Our research efforts have focused on addressing these challenges through harmful text classification, adversarial attack prevention, LLM defenses, and self-evaluation techniques. To counter these threats, we have developed various defense mechanisms, including fine-tuned models and self-evaluation, to enhance LLM security. Our proposed self-evaluation defense mechanism has demonstrated significant effectiveness in safeguarding LLMs against adversarial attacks, maintaining model performance without increasing vulnerability. Practical applications of our solutions include utilizing self-evaluation as a strong defense against unsafe inputs, thereby improving LLM security. For businesses, we offer AI solutions that identify automation opportunities, define KPIs, and gradually implement AI to redefine work processes. Connect with us at hello@itinai.com for AI KPI management advice and continuous insights into leveraging AI for your business. To discover how AI can redefine your sales processes and customer engagement, visit itinai.com. For free consultation and updates, join our AI Lab on Telegram @itinai and follow us on Twitter @itinaicom. We are committed to providing practical AI solutions that enhance safety, reliability, and performance.