Teach your LLM to answer with facts, not fiction

Thoughout this comment you speak about LLMs as-if they're animals, or real physical objects. An LLM is a formal model which is just to generate a sequence of tokens maximally probabilistically consistent with a corpus of historical text.

A digital machine running a LLM program is a physical object which necessarily generates text based on "guessing" because that's the algorithm it's running. LLMs are "guessing algorithms", all of Machine Learning is -- it is dumb brute-force analysis of conditional probability.

> GPT4 is roughly 40 times smaller than a human brain

This doesn't make any sense. GPT4 is an abstract algorithm with no "size". The brain has 10^{big number} cells, and GPT4 can be specified with a single real number. Is that the comparison to make? No, both comparisons are incoherent.

A physical device running GPT4 can be given a "size", but it would again have nothing to do with a brain.

LLMs arent living things where we can "measure their size" and "train them to know, rather than to guess". They are just the equation, `max P(answer|propmt, historical_corpus)`

A machine running GPT4 is just an electrical device generating text according to the rule given above. There is no sense of "training it to do something other than guesswork", and no sense of "size"

Nor do people, though! This is setting the bar way too high.

The whole point to having edited reference sources like "encyclopedias" is that so that we can rely on the expertise of the editors in lieu of having to develop the expertise ourselves[1].

No, an LLM that simply knows a priori (via prompt hacking) which sources are trustworthy would be absolutely comparable to the way an educated-but-non-expert human approaches sources.

[1] Which is a chicken and egg problem anyway. Everyone starts with edited reference sources as tutorial material. Quite frankly everyone starts learning with wikipedia.

wait why is this implied to not be black and white? Charmeleon is the only correct answer.

From the looks of it, they're just advertising a SQL extension that adds vectorization and vector search. Further, it looks like the only thing that the LLM is doing here is deciding which column to run the vector search on. Why is that even necessary? Why are you not pre-processing "vector'd" columns into a normalized format to query against?

They're basically adding an unnecessary LLM step to what amounts to a vector search. In fact, the LLM is essentially blindly deciding which column is the best column to pull an answer from.

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EDIT: Just struck me how terrifyingly dangerous this blog is. Really tired of seeing this crap in the LLM community.

The basic premise of this blog is "give an LLM complete access to your database. Let it decide how and where it should pull data from". This is basically useless without talking about how you prevent the LLM from pulling data from places you don't want it to.

A far better and safer approach remains to push your relevant fields to a separate place for your LLM. In the spirit of this blog, you should just index to a new table. More realistically, you should just put this in a vector store.

RLHF is the difference between GPT-3.5 and ChatGPT, and it's the whole reason why LLMs are suddenly such a big deal. ChatGPT demonstrated that it's possible to give language models a goal beyond just "complete most likely next word" and that they can actually be somewhat competent at achieving those goals despite not being explicitly trained for them.