[R] Program generation is all you need? For math, symbolic reasoning, natural language, etc.

The paper introduces improved performance by prompting LLMs with "natural language embedded programs (NLEP)". No task-specific prompt is needed.

Paper: [https://arxiv.org/abs/2309.10814](https://arxiv.org/abs/2309.10814)

An automatic NLEP generation toolkit is opensourced: [https://github.com/luohongyin/langcode](https://github.com/luohongyin/langcode)

Example Colab notebook is included in the Github repo.

This work introduces the following features of NLEP:

1. NLEP is a full python program that prints the target response of LLMs.

2. Task-general NLEP prompting outperforms task-specific chain-of-thought prompting on math, symbolic, and natural language.

3. Enable the chain-of-thought reasoning ability …

colab embedded etc example features github github repo language llms machinelearning math natural natural language notebook paper performance prompt prompting python reasoning symbolic reasoning work