Meta-prompting can improve the reasoning capabilities of large language models



summary
Summary

Researchers at Stanford University and OpenAI present a method called meta-prompting that can improve the performance of large language models – but also the cost.

Meta-prompting allows a language model to break down complex tasks into smaller, more manageable parts.

These subtasks are then handled by specific “expert” instances of the same language model, each working under specific, customized instructions.

The language model itself acts as a conductor, controlling the communication between these expert models and efficiently integrating the results of these expert models.

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Image: Suzgun, Kalai

This can improve the model’s performance, especially on logical tasks, but the researchers show that it can also help with creative tasks such as writing sonnets.

Complex prompts for complex tasks

Meta-prompting is particularly effective for complex tasks that require reasoning. In the Game of 24, where the goal is to form an arithmetic expression with the value 24 by using each of four given numbers exactly once, the language model suggested consulting experts in mathematics, problem-solving, and Python programming.

The math expert suggested a solution that was recognized as incorrect by a second expert. The language model then suggested writing a Python program to find a valid solution.

A programming expert was brought in to write the program. Another programming expert identified an error in the script, changed it, and ran the revised script.

A mathematics expert was then asked to verify the solution produced by the program. Only after this review did the language model produce the final answer.

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