Broader concepts of making AI helpful, honest, and harmless.
AI Safety and Alignment are broad fields of research and practice focused on ensuring that advanced AI systems are beneficial to humanity. The goal is to build systems that are helpful (they do what we want them to do), honest (they don't deceive us), and harmless (they don't cause negative side effects). RLHF is a primary technique for achieving alignment, but the problem is much broader. One key area of safety is robustness. This involves making models resilient to adversarial attacks, where small, imperceptible changes to an input can cause the model to make a completely wrong prediction. For LLMs, this manifests as 'jailbreaking' or 'adversarial prompting,' where users craft specific prompts to bypass the model's safety filters and elicit harmful, biased, or otherwise forbidden content. 'Red Teaming' is the practice of having a dedicated team of experts actively try to find and exploit these vulnerabilities so they can be fixed. Another area is interpretability and explainability (XAI). Because LLMs are 'black boxes,' it's hard to understand why they make a particular decision. Research in this area aims to develop techniques to peer inside the model and understand its reasoning process, which is crucial for debugging, ensuring fairness, and building trust. A major challenge is mitigating bias. LLMs are trained on vast amounts of internet text, which contains a wide range of human biases. These biases can be encoded into the model's parameters, leading it to generate stereotypical or unfair content. Safety and alignment techniques aim to identify and reduce these biases, though it remains a significant and unsolved problem. Ultimately, alignment is about ensuring that the goals we specify for an AI system align with our true intentions, a problem that becomes increasingly critical as AI systems become more powerful and autonomous.