Answering High-precision Problems for LLMs by Combining Text2code

The current large language models (LLMs) mainly lacks this capability: answering high-precision questions/prompts. LLMs is actually a powerful fuzzy memory system that makes it difficult to answer high-precision questions. The results of code execution is a kind of high-precision answer. And expert system applications need to answer these kinds of questions. In this paper, to solve the above problem, We propose a design of LLMs combined with the text2code approach.