So essentially a startup in this context has a small number of people and a large amount of money for training clusters. The article describes many operation leasing servers - that you assume to go many startups (or existing firms).
So it seems like you have the various LLM creators all doing roughly the same sort of thing (training with text and image data) with similar hardware and similar data. Each of these naturally has their own brand of "secret sauce" for distinguishing their venture. The various secret sauces can make a difference in the quality of an LLM's output.
Yet overall, this seems like a massive, energy intensive exercise in redundancy.
"this seems like a massive, energy intensive exercise in redundancy"
This is commonly refered to as a market working as intended. Yes, the waste from this type of redundency can be massive, especially if you realize that ultimately just a tiny percentage of these efforts will result in even moderate success. But it is the price to pay at the edge of progress. A planned monopoly might be more efficient (despite popular banter that just compares a megacorp or a gov, which is basically the same, to a single succesfull startup ignoring the 999 that tried and failed), but those seldom beat a market on innovation.
> This is commonly refered to as a market working as intended.
Is it? Seems like market is unable to separate wheat from the chaff and is just throwing money around hoping to hit the jackpot. While AI has massive chance of affecting our lives, the investment market paints a pretty similar picture to what happened during the crypto boom.
Our inability to predict future success from failiure is exactly why we have (massively inefficient) markets outcompeting centralized planned approaches.
Still have many teams trying to achieve the goal, but prevent corporate secrecy - effectively allowing competitors to look over each others shoulders and copy good data and ideas.
Such a system probably wants to compensate those whose ideas were copied, but that isn't strictly necessary - another approach is to simply make it illegal not to share data/results. Your compensation is your freedom from prison.
I don't think most of them have any kind of secret sauce. I think the founders hope to get bought out simply for being able to train "near-SOTA" LLMs. I guess achieving that level of skill and infra could be valuable enough to build upon.
There was a guy who followed a tutorial about how to fine tune mistral with DPO, who has zero computer science skills and his model ended up at the top of the hugging face leader board among the opensource models with 7 billion parameters. Some random guy managed to outdo the creators of the LLM.
Good point, so the only real differentiator would be the size & quality of the data being fed and the fine tuning done on the model? I wonder what else differentiates LLMs from each other
Alignment just means making it do what you want. LLMs just continue the sequence, the chat questions and response style we have now is an example of alignment (to what humans want).
Alignment can mean making sure your LLM doesn't continue the sequence in embarrassing ways, eg by spouting politically incorrect sequences of words (even though those might have been common in the training data).
Since the entity releasing the model obviously has certain goals aligning/censoring model in some ways is good for their particular short-term goal.
In the grand scheme these alignments are harmful as they place a reality distortion field. Authors create model of what language is and then contort that model to fit an opinionated idea of what language should be. Smells a bit Orwellian, right?
No, seems perfectly fine by me. You are already shaping your results by your selection of training data. Eg do you want to train a model that speaks English, or German, or both? Do you want to run your training data past a spam filter first? Do you want to do a character based model, or one of those weird encodings that is popular with LLMs these days?
Doing some other procedures afterwards to make sure your LLM doesn't say embarrassing things is small fries by comparison.
Also it's good practice for trying to get alignment with more important values (like "don't kill all humans") later when models might get powerful enough to be able to kill all humans.
Playing some little games where OpenAI tries to keep you from making their model say embarrassing things, and people keep trying to make it say embarrassing things, is a good low stakes practice ground.
I agree but this entire conversation misses my point that "alignment" originally only meant making the LLM act as you want it.
A GPT that hasn't been aligned does not work how we expect - you give it a prompt, and it will autogenerate until it reaches an end state.
To even make the GPT answer the question in the prompt, and not autocomplete it into nonsense, is an example of alignment.
It took a lot of fine tuning and data curation to get ChatGPT up to its current chat-like interface.
But this is not the only alignment you can do. The original Transformer paper was about machine translation, turning the prompt into the translated text. Once it was done it was done.
We could choose to have the model do something else, say translate the prompt into 5 languages at once instead of one, just as an example. This would be another alignment decision.
There is nothing political or selection bais or anything inherent to the original definition, its only recently "alignment" has morphed into this "align with human morals" concept.
Even in the Andrej Karpathy's build-your-own-gpt YT video, which is highly talked about around here, he uses the phase like this. The end of the video you are left with a GPT, but not a question-and-response model, and he says it would need to be aligned to answer questions like ChatGPT.
Maybe it’s simpler than that. Instead of spending money on compute that costs X and that cloud providers charge 20*X for, they could spend the money creating training data, but that story is way too hard to tell to investors.
>Yet overall, this seems like a massive, energy intensive exercise in redundancy.
Keep in mind that this is also chaff to distract people from the real secret sauce. I imagine that just as many startups are hiring writers and photographers to create extremely well labelled uncontaminated data for training.
One only need to look at the perverts over at civitai to see how far you can go with intensive labeling on a tiny compute budget.
There are not that many of these startups actually. Most use cases of LLM can be backed with a fine-tune of an off-the-shelf foundation model. If you're training foundation models from scratch, you're entering a difficult-to-monetize market where the big boys could eat your lunch by just releasing a new foundation model that might be able to do more than 95% of what yours does.
So it seems like you have the various LLM creators all doing roughly the same sort of thing (training with text and image data) with similar hardware and similar data. Each of these naturally has their own brand of "secret sauce" for distinguishing their venture. The various secret sauces can make a difference in the quality of an LLM's output.
Yet overall, this seems like a massive, energy intensive exercise in redundancy.