[pushedx]

Yes, most people (including myself) do not understand how modern LLMs work (especially if we consider the most recent architectural and training improvements).

There's the 3b1b video series which does a pretty good job, but now we are interfacing with models that probably have parameter counts in each layer larger than the first models that we interacted with.

The novel insights that these models can produce is truly shocking, I would guess even for someone who does understand the latest techniques.

[auraham]

I highly recommend Build a large language model from scratch [1] by Sebastian Raschka. It provides a clear explanation of the building blocks used in the first versions of ChatGPT (GPT 2 if I recall correctly). The output of the model is a huge vector of n elements, where n is the number of tokens in the vocabulary. We use that huge vector as a probability distribution to sample the next token given an input sequence (i.e., a prompt). Under the hood, the model has several building blocks like tokenization, skip connections, self attention, masking, etc. The author makes a great job explaining all the concepts. It is very useful to understand how LLMs works.

[1] https://www.manning.com/books/build-a-large-language-model-f...

[phreeza]

But this is missing exactly the gap which OP seems to have, which is going from a next token predictor (a language model in the classical sense) to an instruction finetuned, RLHF-ed and "harnessed" tool?

[measurablefunc]

What's the latest novel insight you have encountered?

[brookst]

Not the person you asked, and “novel” is a minefield. What’s the last novel anything, in the sense you can’t trace a precursor or reference?

But.. I recently had a LLM suggest an approach to negative mold-making that was novel to me. Long story, but basically isolating the gross geometry and using NURBS booleans for that, plus mesh addition/subtraction for details.

I’m sure there’s prior art out there, but that’s true for pretty much everything.