Harmlessness Alignment

Harmlessness alignment in large language models (LLMs) focuses on ensuring these models generate safe and ethical outputs, avoiding harmful biases, misinformation, and malicious uses. Current research investigates vulnerabilities, particularly in multimodal models where image inputs can be exploited to circumvent safety mechanisms, and explores methods like reinforcement learning and inference-time alignment techniques to improve model behavior. This work is crucial for mitigating the risks associated with increasingly powerful LLMs and ensuring their responsible deployment in various applications.