Prompt Repetition Improves Non-Reasoning LLMs (arxiv.org)

A recent study has revealed that repeating input prompts can significantly enhance the performance of popular non-reasoning large language models (LLMs) such as Gemini, GPT, Claude, and Deepseek. This innovative approach demonstrates that by simply repeating the prompt, these models achieve better performance without any increase in token generation or latency, suggesting a straightforward yet effective method for improving model outputs in certain contexts. This finding is particularly significant for the AI/ML community as it opens new avenues for optimizing existing LLMs, especially in applications where reasoning is not primary. The utilization of prompt repetition could lead to more efficient responses and improved user experience while maintaining computational efficiency. As LLMs continue to evolve, understanding and leveraging simple adjustments like this could enhance their applicability in various domains, fostering further advancements in machine learning practices and technologies.