How can LLM efficiency be improved without compromising the quality of generated text?

LLM efficiency can be improved without compromising the quality of generated text by implementing confidence-guided self-refinement methods like CoRefine. This approach works by allowing the model to iteratively refine its outputs based on confidence scores, effectively reducing verbosity while maintaining accuracy. For instance, research has shown that CoRefine can achieve competitive performance with significantly lower computational costs compared to traditional methods, as evidenced by experiments demonstrating reduced token usage while preserving the quality of generated responses.