Self Restraint

Self-restraint in artificial intelligence focuses on developing methods to control and regulate the behavior of large language models (LLMs), preventing undesirable outputs like hallucinations or harmful content. Current research explores techniques like self-reflection and iterative self-evaluation, where models assess their own responses and adjust accordingly, as well as methods that leverage gradient-based control mechanisms to steer model generation towards desired behaviors without extensive human annotation. These advancements are crucial for ensuring the safe and responsible deployment of LLMs, improving their reliability and trustworthiness across various applications.