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Directional Stimulus Prompting
Li et al. (2023) introduces a new way to give prompts that helps large language models (LLMs) produce better summaries.

In this method, a smaller, adjustable model is trained to create helpful hints or suggestions for the LLM. This approach uses reinforcement learning (RL) to fine-tune the process, which is becoming more common in improving LLM performance.

The figure below compares Directional Stimulus Prompting with traditional prompting. The smaller model generates these hints to guide a larger, pre-trained LLM that remains unchanged.