particle-abcs VS openai-cookbook

I thought I'd throw it a curveball:

https://github.com/chbarts/particle-abcs

And my question:

> What language is this written in?

The answer:

> This appears to be a poem written in English. It is written in a form that is often used for teaching purposes, in which each letter of the alphabet is associated with a concept related to a particular field of study. In this case, the poem is about quantum field theory and particle physics. The poem is written in a rhyming couplet form and contains many scientific terms that are specific to the field.

Which is entirely correct. I'm impressed.

A pretrained LLM is a closed-book system: It can only access information that it was trained on. With domain fine-tuning, the system manifests additional material. An early prototype of this technique was shown in this OpenAi cookbook: For the target domain, text was embedded using an API, and then when using the LLM, embeddings were retrieved using semantic similarity search to formulate an answer. Although this approach evolved to retrieval-augmented generation, its still a technique to adapt a Gen2 (2020) or Gen3 (2022) LLM into a question-answering system.

https://github.com/openai/openai-cookbook/blob/main/examples...

It sounds like you are trying to tweak embeddings for similarity search. Rather than fine-tune the model's layers, you may want to try training a linear transformation the existing model's output embedding. Openai has a cookbook on how to do that. You will need some data though - but I think you can try it with ~20 pieces of synthetically generated data.

tutorial from OpenAI https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb

OpenAI Cookbook

OpenAI community repo with lots of examples: https://github.com/openai/openai-cookbook