Data-Free Knowledge Distillation via Reinforced Prompt

In recent years, there has been a growing interest in using large language models (LLMs) for question answering tasks. However, these models are not perfect and can sometimes produce incorrect or incomplete answers. To address this issue, researchers have proposed various methods to improve the credibility of the statements generated by LLMs. One such method is called "inductive reasoning," which involves using specific observations to draw general conclusions.
Inductive reasoning is like solving a puzzle. Imagine you have a bunch of random pieces and you need to find the missing piece to complete the picture. You can use other pieces to help you figure out what the missing piece might look like, and then use that information to find the correct one. Similarly, inductive reasoning involves using specific observations to make educated guesses about what the correct answer might be.
One challenge with inductive reasoning is that it can be time-consuming and computationally expensive. To address this issue, researchers have proposed a new method called "prompting." Prompting involves providing LLMs with a set of demonstrations or examples to help them understand what the correct answer might be. This can make the process faster and more efficient.
There are two types of prompting methods: (1) inductive prompting, which involves using specific observations to draw general conclusions, and (2) trivial prompting, which involves providing LLMs with a set of obvious answers. Inductive prompting is more effective in situations where the correct answer is not immediately clear, while trivial prompting is better suited for tasks that can be solved through straightforward reasoning.
Researchers have tested their proposed method on several datasets and found that it outperforms existing methods in terms of accuracy and efficiency. They also demonstrated that their method can handle complex questions that require multiple reasoning steps, such as arithmetic reasoning and commonsense reasoning.
In summary, the article proposes a new method called prompting to improve the credibility of statements generated by LLMs. Prompting involves providing LLMs with a set of demonstrations or examples to help them understand what the correct answer might be, and there are two types of prompting methods: inductive prompting and trivial prompting. The proposed method has been shown to outperform existing methods in terms of accuracy and efficiency, and it can handle complex questions that require multiple reasoning steps.