Also, not all language models are transformers. You can have language models based on diffusion models or state-space models, or any other model that can be used to model sequences (so all of them, as sequences are just functions).
Transformers are just the ones that are most common and successful today.
I'd argue that meaningful sequences of symbols constitute a language. This example doesn't use a human language but it does use a language IMO (at least AFAIU).
I don't understand what you're saying? An LLM is a transformer model trained on a large corpus of natural language, often with some post-training. An image model is a different type of transformer model. What's controversial here?
Your example is a meaningless sequence. So consider a different scenario where the sequence is meaningful but does not map to any human language. What exactly disqualifies it as a non-human language?
When you encounter a human language that you can't personally read presumably you don't proceed to claim that it doesn't constitute language on the basis of your own lack of ability.
To come at it from a slightly different angle - does compiled binary code count as a language? If not, why not? (I'll suggest that it's a language albeit not a natural one.)
Any sequence could be meaningful or meaningless depending on the grammar involved. Open a word document in photoshop and the program has no idea what it’s dealing with.
The reason the language term in LLM is meaningful is how the training, symbol mapping, etc is designed around human languages. The model doesn’t process raw text, instead there’s a critical processing step which allows the magic to happen.
To my mind the argument against this model qualifying as a language model is that while the sequence of tokens may technically qualify as a sort of language it doesn't appear to be generalized by any reasonable interpretation. Further, the model doesn't appear to be able to handle unstructured inputs and outputs in the "language" - everything seems to be highly structured.
My line of reasoning could be approximately summarized as compiled binaries constituting a "real" (though not natural) language versus a sequential listing of chess positions that represent sequential game actions only being language "shaped".
Still, it's interesting to consider that if scaled up I expect the "repeat yourself" experiment would likely apply to the internal representation of the model in the same manner.
The distinction is meaningful because the process described here isn’t bound by the same constraints, resulting in meaningful consequences.
Suppose we flipped the initial stings and fed that into the process. There would still be meaning to extract from the training set but our new Reversed English but it is not English so the preprocessing step can’t be based on that assumption.
I don't understand what point you're trying to make here. At the risk of being tangential (due to not understanding) LLMs have famously been able to accept base64 encoded natural language as input "out of the box" because (it seems) under the hood they already transparently translate all inputs into their own internal "language" system on the fly. Presumably a similar translation could be learned for input of reverse english (or rather merely adjusting attention) although at a glance it seems like output might not be feasible.
Pretty sure it's one of those "All squares are rectangles but not all rectangles are squares" situations.
Transformers are what really started the LLM Boom, and seem to be crucial to the technology. They also have other applications, such as what OP created
It gets extremely blurry, because people commonly refer to any model that uses a component associated with the Transformer architecture as a Transformer (i.e. using some kind of QKV-esque attention mechanism). I think it's easier to think of it like this:
A large language model is just what it says--a very large statistical model trained for language tasks. This covers the spectrum of GPT-style models, but also those hard to classify ones, like Liquid's "Liquid Foundation Models", which can get up to 24 billion parameters and use grouped query attention, but are closely related to state-space models as well: https://huggingface.co/LiquidAI/LFM2-24B-A2B
Also, as others have pointed out, a Transformer isn't inherently a language model. So really they're sort of two different axes, one classifying the model size and task, the other referring to a specific architecture.
In my unpopular opinion, it wasn't transformers or attention, but pretraining on language data that kicked off the LLM Boom. Alec radford in his little jupyter notebook trained a very small non-transformer to predict simply the next-character on Amazon reviews. He noticed emergence of a neuron which when toggled controlled the sentiment of the remaining text. This is published as the sentiment neuron paper.
Till then, transformers were being used primarily for stuff like translation and such and no one was even pretraining at scale, even tho transformers and attention existed.
Openai and google if you count T5 persisting with pretrained generative models was what led to the LLM boom. Yes they used transformers, but that's just one IMO minor aspect.
Wasn't it an LSTM neuron that changed sign and magnitude over a stretch of text? I remember seeing color coding against the current sentiment which was neat.
Technically correct, but not in a functionally useful way.
The “L” in LLM’s generally refers to human-language specifically. You’d expect to feed it…human text. Nitpicking that the human text also constitutes a mathematical language is like, correct, but so general as to be unhelpful.
Super cool project! Though, aren't most modern LLM's ar transformers internally?