  Recent AI-assistant agents, such as ChatGPT, predominantly rely on supervised
fine-tuning (SFT) with human annotations and reinforcement learning from human
feedback (RLHF) to align the output of large language models (LLMs) with human
intentions, ensuring they are helpful, ethical, and reliable. However, this
dependence can significantly constrain the true potential of AI-assistant
agents due to the high cost of obtaining human supervision and the related
issues on quality, reliability, diversity, self-consistency, and undesirable
biases. To address these challenges, we propose a novel approach called
SELF-ALIGN, which combines principle-driven reasoning and the generative power
of LLMs for the self-alignment of AI agents with minimal human supervision. Our
approach encompasses four stages: first, we use an LLM to generate synthetic
prompts, and a topic-guided method to augment the prompt diversity; second, we
use a small set of human-written principles for AI models to follow, and guide
the LLM through in-context learning from demonstrations (of principles
application) to produce helpful, ethical, and reliable responses to user's
queries; third, we fine-tune the original LLM with the high-quality
self-aligned responses so that the resulting model can generate desirable
responses for each query directly without the principle set and the
demonstrations anymore; and finally, we offer a refinement step to address the
issues of overly-brief or indirect responses. Applying SELF-ALIGN to the
LLaMA-65b base language model, we develop an AI assistant named Dromedary. With
fewer than 300 lines of human annotations (including < 200 seed prompts, 16
generic principles, and 5 exemplars for in-context learning). Dromedary
significantly surpasses the performance of several state-of-the-art AI systems,
including Text-Davinci-003 and Alpaca, on benchmark datasets with various
settings.
