Pre-trained Large Language Models (LLMs), like GPT-3, have proven to have extraordinary aptitudes for comprehending and replying to questions from humans, helping with coding chores, and more. However, they frequently generate outcomes that differ from what people like. In the past, researchers have attempted to resolve this problem by gathering information on human preferences and...
Large Language Models (LLMs) are pre-trained models that have exceptional abilities in understanding and responding to human questions.
LLMs can assist with various tasks such as comprehending and replying to questions, aiding in coding chores, and more.
LLMs often produce results that differ from human preferences.
Researchers have tried to resolve this issue by gathering information and feedback from humans.
RAIN is a self-evaluation system designed to help LLMs assess their own outputs for safety.
Yes, with the help of systems like RAIN, LLMs can evaluate their outputs for safety.
Self-evaluation for safety allows LLMs to generate outputs that align better with human preferences and reduce potential risks.
RAIN is a system that enables LLMs to assess their outputs by comparing them to human feedback and predefined safety guidelines.
Self-evaluation is crucial for LLMs to ensure their outputs are safe, reliable, and aligned with human values.
While RAIN helps in improving outcomes, complete elimination of differing outcomes may still be a challenge.