This is the code that backs the blog post at: https://rushabhdoshi.com/posts/2023-09-18-llm-leverage/

This is based on the GPTs are GPTs paper. I was interested in building an intuition around the broad impacts of LLMs and used that as a starting point.

This repo is essentially three parts:

1. Building up a sqlite db from SQL files from O*Net. The actual files aren't included because they're large. You can download them here: https://www.onetcenter.org/database.html#all-files
2. Classifying the DWAs from the O*Net using OpenAI. This is done in notebooks/classification.ipynb. The results are stored in data/readonly
3. Analyzing the results. This is done in notebooks/analysis.ipynb. The results are stored in data/output

To get up and running, after downloading the repo, do:

poetry install

I prefer to run jupyter notebooks in VSCode but YMMV.

Contributions are welcome, especially if you see bugs and issues. Please open a PR.

• Replicate with GPT-4 (costs quite a bit more)
• Look at gpt-3.5-turbo-instruct with log-probs, to see if it improves classification
• Classify tasks instead of DWAs
• Classify occupations instead of DWAs