LLMs have strong capabilities to generate text. Using effective prompt strategies can steer the model to produce better, more consistent, and more factual responses. LLMs can also be especially useful for generating data which is useful to run all sorts of experiments. For example, we can use it to generate quick samples for a sentiment classifier like so:
Produce 10 exemplars for sentiment analysis. Examples are categorized as either positive or negative. Produce 2 negative examples and 8 positive examples. Use this format for the examples:
[Gao et al., (2022)](https://arxiv.org/abs/2211.10435) presents a method that uses LLMs to read natural language problems and generate programs as the intermediate reasoning steps. Coined, program-aided language models (PAL), differ from chain-of-thought prompting in that instead of using free-form text to obtain a solution it offloads the solution step to a programmatic runtime such as a Python interpreter.
Let's look at an example using LangChain and OpenAI GPT-3. We are interested to develop a simple application that's able to interpret the question being asked and provide an answer by leveraging the Python interpreter.
Specifically, we are interested to create a function that allows the use of the LLM to answer questions that require date understanding. We will provide the LLM a prompt that includes a few exemplars that are adopted from [here](https://github.com/reasoning-machines/pal/blob/main/pal/prompt/date_understanding_prompt.py).
|Learn how to use code as reasoning for solving common tasks using the Python interpreter in combination with the language model.|[PAL: Code as Reasoning](../notebooks/pe-pal.ipynb)|