I’m afraid that would not be sufficient.

These instructions are a small part of what makes a model answer like it does. Much more important is the training data. If you want to make a racist model, training it on racist text is sufficient.

Great care is put in the training data of these models by AI companies, to ensure that their biases are socially acceptable. If you train an LLM on the internet without care, a user will easily be able to prompt them into saying racist text.

Gab is forced to use this prompt because they’re unable to train a model, but as other comments show it’s pretty weak way to force a bias.

The ideal solution for transparency would be public sharing of the training data.

people who’ve never been laid

That was unnecessary. I know that people with poor social skills have more trouble with romance, but implying that all virgins are socially inept is a harmful stereotype, luck is a big factor in finding relationships.

It’s absolutely amazing, but it is also literally and technologically impossible for that to spontaneously coelesce into reason/logic/sentience.

This is not true. If you train these models on game of Othello, they’ll keep a state of the world internally and use that to predict the next move played (1). To execute addition and multiplication they are executing an algorithm on which they were not explicitly trained (although the gpt family is surprisingly bad at it, due to a badly designed tokenizer).

These models are still pretty bad at most reasoning tasks. But training on predicting the next word is a perfectly valid strategy, after all the best way to predict what comes after the “=” in 1432 + 212 = is to do the addition.

Now let’s look at Office. Open an Excel spreadsheet with tables in any app other than excel. Tables are something that’s just a given in excel, takes 10 seconds to setup, and you get automatic sorting and filtering, with near-zero effort. No, I’m not setting up a DB in an open-source competitor to Access. That’s just too much effort for simple sorting and filtering tasks, and isn’t realistically shareable with other people.

Am I missing something or isn’t it exactly the same thing in libre office ?

I don’t believe that there are solutions that are as complete as team, for video and voice calls it’s among the best.

But it’s so bad for text ! Why do I have to wait for a second when I change channels ? Why does it not support markdown (the partial implementation that it has is arguably worse than no implementation at all) ? Why is the search so bad ?

And cow feed is also grown with tons of pesticides and you need much more of it for less tissue at the end.

I have hard time seeing clothing with a bigger environmental than leather.

Convolutional neural networks and plant identifying apps came before chat gpt. Beyond both relying on neural networks they don’t have much in common.

Don’t know why you are down voted it’s a good question.

As a matter of fact it almost happened for search engines in France. Newspaper’s argued that snippets were leading people to not go into their ad infested sites thus losing them revenue.

https://techcrunch.com/2020/04/09/frances-competition-watchdog-orders-google-to-pay-for-news-reuse/

Yes to your question, but that’s not what I was saying.

Here is one of the most popular training datasets : https://pile.eleuther.ai/

If you look at the pdf describing the dataset, you’ll find the mean length of these documents to be somewhat short with mean length being less than 20kb (20000 characters) for most documents.

You are asking for a model to retain a memory for the whole duration of a discussion, which can be very long. If I chat for one hour I’ll type approximately 8400 words, or around 42KB. Longer than most documents in the training set. If I chat for 20 hours, It’ll be longer than almost all the documents in the training set. The model needs to learn how to extract information from a long context and it can’t do that well if the documents on which it trained are short.

You are also right that during training the text is cut off. A value I often see is 2k to 8k tokens. This is arbitrary, some models are trained with a cut off of 200k tokens. You can use models on context lengths longer than that what they were trained on (with some caveats) but performance falls of badly.

There are two issues with large prompts. One is linked to the current language technology, were the computation time and memory usage scale badly with prompt size. This is being solved by projects such as RWKV or mamba, but these remain unproven at large sizes (more than 100 billion parameters). Somebody will have to spend some millions to train one.

The other issue will probably be harder to solve. There is less high quality long context training data. Most datasets were created for small context models.