ChatGPT3.4/4 tokens:
“Elephant”: 46439, 28022 - “Ele” “phant”
“elephant”: 10274, 28022 - “ele” “phant”
“ Elephant”: 79189
“ elephant”: 46840
“ elephantine”: 46840, 483 - “ elephant” “ine”
Regarding the ‘no idea which future it is going to follow’ - sure, it doesn’t know which future; indeed the sampler phase is going to pick an output merely based on the probabilities it’s outputting. But it’s outputting higher probabilities for some tokens because they are good tokens to use to lead to probable futures. It’s suggesting taking steps down certain paths because those paths are likely to lead to useful places.
But, it doesn't make any difference whether you are considering tokens or words. There are multiple possible continuations of the prompt, and the next word (or token) output does not - in general - force the word (or token) after that ...
Your "large grey mammal" could be an "elected official in a grey suit".
Likewise when a coding LLM starts outputting a for each loop, it’s doing so because it expects to want to write some code that operates on each item in a list. I don’t see how you can explain that behavior without thinking that it must be generating some sort of high level algorithmic plan that causes it to feel like the next thing it should output is some sort of ‘foreach’ token.
Of course next word predictions are not based directly on surface level word sequence patterns - they are based on internal representations of what these word sequences mean, and predicted continuations are presumably going to be at a similar level of abstraction/representation (what you are calling a plan). This continuation "plan" then drives actual word selection/prediction.
Where we seem to differ is whether this high level continuation representation can really be considered as a "plan". To me the continuation is just a prediction, as are the words that might be used to start expressing that continuation, and presumably it's not even a single continuation with multiple ways of expressing it (turning it into a word sequence), but rather some superposition of multiple alternate continuations.
When we get to the level of words output it becomes even less plan-like since the actual word output is randomly sampled, and when fed back in as part of the "sentence so far" may cause the model to predict a different continuation (or set of continuations) than it had at the prior step. So, any "plan" (aka predicted continuation) is potentially changing continuously from word to word, rather than being decided ahead of time and then executed. As I noted elsewhere in this thread, the inability to plan multiple words ahead is behind these model's generally poor performance on the "give me a sentence ending in <word>" task, as opposed to perfect performance on the "give me a sentence starting with <word>" one.
If we contrast this behavior of a basic LLM to the "tree of thoughts" mechanism that has been proposed, it again highlights how unplan-like the basic behavior is. In the tree of thoughts mechanism the model is sampled from multiple times generating multiple alternate (multi-word) continuations, which are then evaluated with the best being chosen. If the model were really planning ahead of time it seems this should not be necessary - planning would consist of considering the alternatives BEFORE deciding what to generate.
Virtually all modern transformer models use pieces, which may be words, but also subwords. Theoretically, they could be longer units, but in most cases some characters (like whitespace) are used as piece boundaries when training the piece vocabulary. If they didn’t use pieces, they’d work terribly on languages where e.g. compounds are a single word.
In most realistic piece vocabs, ‘elephant’ will be a single piece, since it’s a fairly frequent word. But it’s totally possible in a small vocab that it would be split like the parent said and conversely, it would generate elephant by first predicting one piece.
Some piecing methods, like BBPE have bytes as the smallest unit, so theoretically an unknown token could be split up (and generated) as pieces consisting of bytes.
The model outputs words, not tokens, so that is not a great example.
Any prompt will have multiple possible (predict next word) continuations, which you can think of as branching futures. Many possible next words, each of which have many possible following words, etc, etc.
The model is essentially predicting over all these possible futures. You can call it planning if you like, but remember that the model has no idea of which of these branching futures it is going to follow - it literally doesn't even know which word it is going to output next - it is just providing a bunch of probabilities (predictions) of next word, and the sampling process is then picking one - not necessarily the most confident next word prediction.
The model really is winging it word by word, even if those (multiple alternative) next words are only probable because they are part of coherent following sentences in the training data.