Logically Consistent Reasoning with Recursive Expla-nations

In this paper, Jung et al. propose a new technique called "maieutic prompting" to improve the explainability and accuracy of large language models (LLMs). LLMs are trained on vast amounts of data and have demonstrated remarkable abilities in various natural language processing tasks, but their inner workings are often difficult to understand. Maieutic prompting addresses this issue by providing logical and consistent reasoning with recursive explanations.
To understand how maieutic prompting works, imagine a LLM as a complex machine with many interconnected parts. Just like a car’s engine has various components that work together to make it run smoothly, a LLM consists of multiple layers and modules that cooperate to generate coherent text. Maieutic prompting is like providing detailed instructions for each part of the engine, ensuring that they all work together seamlessly.
The authors propose a two-stage training process for maieutic prompting. In the first stage, they fine-tune the LLM on a specific task using a small set of annotated examples. This process helps the model learn to generate coherent and relevant text given a particular context. In the second stage, they use a large dataset of unannotated examples to train the model to provide logical and consistent reasoning with recursive explanations.
The key innovation of maieutic prompting is the ability to generate explanations that are both detailed and hierarchical. Imagine you’re trying to explain a complex concept to a friend, and you want to provide a clear and step-by-step explanation. Maieutic prompting works similarly, breaking down the reasoning process into smaller, manageable parts and providing logical connections between them.
The authors demonstrate the effectiveness of maieutic prompting on several benchmark datasets, achieving state-of-the-art results in various natural language processing tasks. They also show that their approach is more interpretable and easier to understand than existing techniques, making it a valuable tool for developing more responsible AI models.
In summary, maieutic prompting is a powerful technique for improving the explainability and accuracy of large language models. By providing logical and consistent reasoning with recursive explanations, it helps developers understand how these complex models work and improve their performance in various natural language processing tasks.