Self-Refine: Iterative Refinement with Self-Feedback for Language Models (selfrefine.info)

Summary:

• Researchers present Self-Refine, a method for improving large language models' performance by iteratively refining outputs with self-feedback.
• The approach doesn't require supervised training data or reinforcement learning and works with a single language model.

Key terms:

• Self-Refine: A method designed to improve large language models' performance by iteratively refining outputs with self-feedback
• FEEDBACK: A component that receives the initial output and provides task-dependent feedback on how to enhance it
• REFINE: A component responsible for refining the output based on the feedback received from the feedback module
• Iterative creation with feedback description: A process where the model continuously refines its output through a FEEDBACK → REFINE → FEEDBACK loop
• Actionable feedback: Feedback that pinpoints specific areas for refinement and provides instructions on how to improve the output

Tags:

ChatGPT Research GPT-4 GPT-3 Language Models Feedback Unsupervised Learning Code Optimization Output Improvement Refinement