First of all, this link is just to C# bindings of llama.cpp and so doesn’t contain the actual implementation. But it also doesn’t refute my criticism of your claim. More specifically, I take issue with this statement that you said: “today’s LLM’s ingest a ton of text [snip] and builds up statistics of which tokens it sees in that context”.
I claim that this is not how today’s LLMs work because we have no idea what LLMs do with the input data during training. We have very little insight into what kind of data structure it builds and how the data structure it built is organized.
No, it’s done because one letter at a time is too slow. Tokens are a “happy” medium tradeoff.
I think I worded my sentence ambiguously, let me re-word it for you: “Going one token at a time is only considered a limitation because LLMs are not accurate enough”
It makes a “break” of the block, which lets it start a new answer instead of continuing on the previous. How it reacts to that depends on the fine tune and filters before the data hits the LLM.
Once again, my sentence was not written well, my bad. I was commenting on the observed behavior, not on how it works from a technical perspective.
I have just said that LLM’s we have today can’t fix the problems with false data and hallucinations, because it’s a core principle of how it operates. It will require a new approach.
You could add a rocket engine and wings to a pogo stick, but then it’s no longer a pogo stick but an airplane with a weird landing gear. Today’s LLM’s could give us hints to how to make a better AI, but that would be a different thing than today’s LLM’s. From what has been leaked from OpenAI GPT4 has scaling issues so they use mixture of experts. Just throwing hardware at it is already showing diminishing returns. And we’re learning fascinating new ways of training them, but the inherent problem is the same.
Alright, we agree here for the most part so I’m just going to skip this.
For example, if you ask an LLM if it can give an answer to a question, it will have two paths to go down, positive and negative. Note, at the point where it chooses that it doesn’t know how to finish it, it doesn’t look ahead.
This is weird though. How do you know LLMs can’t look ahead? When we prompt LLMs, we are basically asking them this question: “What is the next word of your response?” How do you know it hasn’t written out the entire response in memory already after which it only shows you the first word? LLMs are neural networks. Neural networks have working memory. That’s how neural networks work after all, it’s just a vector of data that is repeatedly transformed as it passes through each layer. Of course, if it does write the entire response in memory, it is all thrown away after every word.
As far as the backspace tokens go, you are right to be skeptical but also do not be surprised if it works out. We’ve had LLMs trained to complete and edit text for some time already. They’ve fallen out of use today but they did perform acceptably well.