Context Injection

Context injection involves strategically introducing additional information—context—into machine learning models to influence their behavior, with applications ranging from improving speech recognition accuracy to enhancing the safety and security of large language models (LLMs). Current research focuses on developing effective context injection techniques, often utilizing transformer-based architectures and reinforcement learning algorithms, to achieve specific goals like exploiting game opponents, mitigating adversarial attacks, or improving model robustness. This area is significant because it addresses critical challenges in AI safety, security, and performance, impacting the reliability and trustworthiness of various AI systems across diverse applications.