renchuw

Create SQL by over fitting decision tree on data, then optimize the boolean representation.

Well, this method is based on the assumption that embeddings can accurately represent the texts and their structural relations are preserved.

So long as you have all the random seeds fixed, I think reproduction should be straight forward.

Thanks for the feedback! The reason the "code" part is more complete than the "research" part is because I originally planned for it to just be a hobby project and only very later on decided to perhaps try to be serious and make it a research work.

Not trying to make excuses tho. Your points are very valid and I would take them into account!

Correct.

Hi, OP here. I would kind of have to disagree here. You raised some interesting points, but I don't think something can be qualified as *moat* if it is overcome-able by just sharing the use cases. For example, we all know Google's use cases is to search, but no one has built one as well as they do. Their moat is in their technology and brand recognision.

This would be an inner loop process. However, the selection is way faster than LLMs so it shouldn't be noticable (hopefully).

Hi, OP here. I would say not really because the goals are different. Although both uses retrieval techniques, RAG wants to augment your query with factual information, where here we retrieve in order to evaluate on as few queries as possible (with performance guaranteed by bayesian optimization)

I designed 2 modes in the project, exploration mode and exploitation mode.

Exploration mode uses entropy search to explore the latent space (used for evaluating the LLM on the selected corpus to evaluate), and eploitation mode is used to figure out how well / bad the model is performing on what regions of the selected corpus.

For accurate evaluations, exploration is used. However, I'm also working on a visualization too s.t. users can see how well the model is performing at what region (courtesy of gaussian process models built in by bayesian optimization) and that is where exploitation mode can come in handy.

Sorry for the slightly messy explanation. Hope it clarifies things!

Perhaps I should clarify it in the project README. It's the phase to evaluate how well your model is performing. So the pipeline goes training -> evaluation -> deployment (inference) corresponding to the datasets in supervised training, training (training) -> evaluation (validation) -> deployment (testing).

Fair question.

Evaluate refers to the phase after training to check if the training is good.

Usually the flow goes training -> evaluation -> deployment (what you called inference). This project is aimed for evaluation. Evaluation can be slow (might even be slower than training if you're finetuning on a small domain specific subset)!

So there are [quite](https://github.com/microsoft/promptbench) [a](https://github.com/confident-ai/deepeval) [few](https://github.com/openai/evals) [frameworks](https://github.com/EleutherAI/lm-evaluation-harness) working on evaluation, however, all of them are quite slow, because LLM are slow if you don't have infinite money. [This](https://github.com/open-compass/opencompass) one tries to speed up by parallelizing on multiple computers, but none of them takes advantage of the fact that many evaluation queries might be similar and all try to evaluate on all given queries. And that's where this project might come in handy.

Hi, OP here. It's not 10 times faster inference, but faster evaluation. You use evaluation on a dataset to check if your model is performing well. This takes a lot of time (might be more than training if you are just finetuning a pre-trained model on a small dataset)!

So the pipeline goes training -> evaluation -> deployment (inference).

Hope that explanation helps!

Hi, OP here. So you evaluate LLMs on corpuses to evaluate their performance right? Bayesian optimization is here to select points (in the latent space) and tell the LLM where to evaluate next. To be precise, entropy search is used here (coupled with some latent space reduction techniques like N-sphere representation and embedding whitening). Hope that makes sense!

Hi, OP here, sorry for late reply. I am not actually "evaluating", but rather using the "side effects" of bayesian optimization that allows zoning in/out on some regions on the latent space. Since embedders are so fast compared to LLM, it saves time by saving LLMs from evaluating on similar queries. Hope that makes sense!

Side note:

OP here, I came up with this cool idea because I was chatting with a friend about how to make LLM evaluations fast (which is so painfully slow on large datasets) and realized that somehow no one has tried it. So I decided to give it a go!